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IEEE DOI
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0409
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0509
BibRef
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IEEE DOI
0408
BibRef
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0407
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0509
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Earlier:
Feasible Adaptation Criteria for Hybrid Wavelet:
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Springer DOI
0311
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Elsevier DOI
0509
BibRef
And:
Experimental Comparison of Combination Rules using Simulated Data,
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IEEE DOI
0609
BibRef
Zouari, H.,
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Lecourtier, Y.,
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IEEE DOI
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Jain, A.K.,
Punch, W.F.,
Clustering Ensembles: Models of Consensus and Weak Partitions,
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IEEE DOI
0512
First uniform representation for multiple classifiers.
Probabilistic model of consensus.
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Minaei-Bidgoli, B.,
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0409
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1208
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1208
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Springer DOI
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Aksela, M.[Matti],
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Using diversity of errors for selecting members of a committee
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PR(39), No. 4, April 2006, pp. 608-623.
Elsevier DOI Classifier combining; Committee classifier; Diversity; Diversity of errors
0604
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Aksela, M.,
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0209
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Ortiz-Boyer, D.[Domingo],
Improving Multiclass Pattern Recognition by the Combination of Two
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PAMI(28), No. 6, June 2006, pp. 1001-1006.
IEEE DOI
0605
BibRef
García-Pedrajas, N.[Nicolás],
Ortiz-Boyer, D.[Domingo],
A cooperative constructive method for neural networks for pattern
recognition,
PR(40), No. 1, January 2007, pp. 80-98.
Elsevier DOI
0611
Constructive algorithms; Pattern classification; Evolutionary computation;
Neural networks; Cooperative coevolution
BibRef
Garcia-Pedrajas, N.[Nicolas],
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PR(42), No. 9, September 2009, pp. 1742-1760.
Elsevier DOI
0905
Classification; Ensembles of classifiers; Boosting; Supervised projections
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de Haro-García, A.[Aida],
Cerruela-García, G.[Gonzalo],
García-Pedrajas, N.[Nicolás],
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PR(96), 2019, pp. 106959.
Elsevier DOI
1909
Instance selection, Boosting, Instance-based learning
BibRef
Dara, R.A.[Rozita A.],
Kamel, M.S.[Mohamed S.],
Wanas, N.M.[Nayer M.],
Data dependency in multiple classifier systems,
PR(42), No. 7, July 2009, pp. 1260-1273.
Elsevier DOI
0903
Multiple classifier systems; Data dependency; Aggregation methods;
Stability; Class imbalance
BibRef
Chen, L.[Lei],
Kamel, M.S.[Mohamed S.],
A generalized adaptive ensemble generation and aggregation approach for
multiple classifier systems,
PR(42), No. 5, May 2009, pp. 629-644.
Elsevier DOI
0902
Pattern recognition; Data mining; Classification; Classifier
combination; Multiple classifier systems
BibRef
Hu, T.M.[Tian-Ming],
Yu, Y.[Ying],
Xiong, J.Z.[Jin-Zhi],
Sung, S.Y.[Sam Yuan],
Maximum likelihood combination of multiple clusterings,
PRL(27), No. 13, 1 October 2006, pp. 1457-1464.
Elsevier DOI
0606
Consensus clustering; Centroid clustering; Markov random field;
Metric distance function
BibRef
Rooney, N.[Niall],
Patterson, D.[David],
A weighted combination of stacking and dynamic integration,
PR(40), No. 4, April 2007, pp. 1385-1388.
Elsevier DOI
0701
Ensemble learning; Stacking; Regression
BibRef
Rooney, N.[Niall],
Patterson, D.[David],
Nugent, C.[Chris],
Non-strict heterogeneous Stacking,
PRL(28), No. 9, 1 July 2007, pp. 1050-1061.
Elsevier DOI
0704
Ensemble learning; Meta learning; Regression
BibRef
Hu, Q.H.[Qing-Hua],
Yu, D.R.[Da-Ren],
Xie, Z.X.[Zong-Xia],
Li, X.D.[Xiao-Dong],
EROS: Ensemble rough subspaces,
PR(40), No. 12, December 2007, pp. 3728-3739.
Elsevier DOI
0709
Ensemble systems initially improve performance as more added,
the decrease.
Attribute reduction; Ensemble learning; Multiple classifier system;
Rough set; Selective ensemble
BibRef
Jarillo, G.[Gabriel],
Pedrycz, W.[Witold],
Reformat, M.[Marek],
Aggregation of classifiers based on image transformations in biometric
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MVA(19), No. 2, March 2008, pp. 125-140.
Springer DOI
0802
BibRef
Kim, Y.W.[Young-Won],
Oh, I.S.[Il-Seok],
Classifier ensemble selection using hybrid genetic algorithms,
PRL(29), No. 6, 15 April 2008, pp. 796-802.
Elsevier DOI
0803
Multiple classifier combination; Ensemble selection; Genetic algorithm;
Local search operation
See also Local search-embedded genetic algorithms for feature selection.
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Asdornwised, W.[Widhyakorn],
Jitapunkul, S.[Somchai],
Multiple Description Pattern Analysis: Robustness to Misclassification
Using Local Discriminant Frame Expansions,
IEICE(E88-D), No. 10, October 2005, pp. 2296-2307.
DOI Link
0510
Apply to ATR task.
BibRef
Carneiro, G.[Gustavo],
Vasconcelos, N.M.[Nuno M.],
Minimum Bayes error features for visual recognition,
IVC(27), No. 1-2, January 2009, pp. 131-140.
Elsevier DOI
0811
BibRef
Earlier:
Minimum Bayes Error Features for Visual Recognition by Sequential
Feature Selection and Extraction,
CRV05(253-260).
IEEE DOI
0505
Visual recognition; Feature selection; Feature extraction;
Minimum Bayes error; Mixture models; Face recognition;
Texture recognition; Object recognition
BibRef
Martínez-Muñoz, G.[Gonzalo],
Hernández-Lobato, D.[Daniel],
Suárez, A.[Alberto],
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation,
PAMI(31), No. 2, February 2009, pp. 245-259.
IEEE DOI
0901
reduce size of ensembles for classifiers.
BibRef
Hernández-Lobato, D.[Daniel],
Martínez-Muñoz, G.[Gonzalo],
Suárez, A.[Alberto],
Statistical Instance-Based Pruning in Ensembles of Independent
Classifiers,
PAMI(31), No. 2, February 2009, pp. 364-369.
IEEE DOI
0901
BibRef
Bacauskiene, M.[Marija],
Verikas, A.[Antanas],
Gelzinis, A.[Adas],
Valincius, D.,
A feature selection technique for generation of classification
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PR(42), No. 5, May 2009, pp. 645-654.
Elsevier DOI
0902
Feature selection; Variable selection; Classification committee;
Genetic search; Support vector machine; Laryngeal image
BibRef
Chaudhuri, P.[Probal],
Ghosh, A.K.[Anil K.],
Oja, H.[Hannu],
Classification Based on Hybridization of Parametric and Nonparametric
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PAMI(31), No. 7, July 2009, pp. 1153-1164.
IEEE DOI
0905
Deal with problems where parametric classifier assumptions fail, but a small
number of training samples cause nonparameteric failures. Combine
strengths of each.
BibRef
Couturier, S.[Stéphane],
Mas, J.F.[Jean-François],
Mountrakis, G.[Giorgos],
Watts, R.[Raymond],
Luo, L.[Lori],
Wang, J.[Jida],
Developing Collaborative Classifiers using an Expert-based Model,
PhEngRS(75), No. 7, July 2009, pp. 831-844.
WWW Link.
0910
A novel framework for integrating classifiers of variable complexity
as applied to specific portions of the input space.
BibRef
Correa-Morris, J.[Jyrko],
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Alvarez-Nadiozhin, D.R.[Denis R.],
An incremental nested partition method for data clustering,
PR(43), No. 7, July 2010, pp. 2439-2455.
Elsevier DOI
1003
Nested partition; Data clustering; Incremental clustering
BibRef
Correa-Morris, J.[Jyrko],
Ruiz-Shulcloper, J.[Jose],
Espinosa-Isidron, D.L.[Dustin L.],
Pons-Porrata, A.[Aurora],
Incremental nested partition method,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Vega-Pons, S.[Sandro],
Correa-Morris, J.[Jyrko],
Ruiz-Shulcloper, J.[José],
Weighted partition consensus via kernels,
PR(43), No. 8, August 2010, pp. 2712-2724.
Elsevier DOI
1006
BibRef
Earlier:
Weighted Cluster Ensemble Using a Kernel Consensus Function,
CIARP08(195-202).
Springer DOI
0809
Cluster ensemble; Kernel function; Similarity measure; Clustering
validity index; Consensus partition
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Correa-Morris, J.[Jyrko],
An indication of unification for different clustering approaches,
PR(46), No. 9, September 2013, pp. 2548-2561.
Elsevier DOI
1305
Data clustering; Clustering function; Impossibility theorem
BibRef
Correa-Morris, J.[Jyrko],
Hernández, N.[Noslen],
On the Comparison of Structured Data,
CIARP12(479-486).
Springer DOI
1209
BibRef
Khreich, W.[Wael],
Granger, E.[Eric],
Miri, A.[Ali],
Sabourin, R.[Robert],
Iterative Boolean combination of classifiers in the ROC space:
An application to anomaly detection with HMMs,
PR(43), No. 8, August 2010, pp. 2732-2752.
Elsevier DOI
1006
BibRef
And:
Boolean Combination of Classifiers in the ROC Space,
ICPR10(4299-4303).
IEEE DOI
1008
Receiver operating characteristics; Combination of classifiers;
Limited and imbalanced data; Hidden Markov models; Anomaly detection;
Computer and network security
BibRef
Khreich, W.[Wael],
Granger, E.[Eric],
Miri, A.[Ali],
Sabourin, R.[Robert],
On the memory complexity of the forward-backward algorithm,
PRL(31), No. 2, 15 January 2010, pp. 91-99.
Elsevier DOI
1001
Hidden Markov Models; Forward-backward; Baum-Welch; Forward Filtering
Backward Smoothing; Complexity analysis
Comment:
See also Comment on the paper 'On the memory complexity of the forward-backward algorithm,'.
BibRef
Connolly, J.F.[Jean-François],
Granger, E.[Eric],
Sabourin, R.[Robert],
Evolution of heterogeneous ensembles through dynamic particle swarm
optimization for video-based face recognition,
PR(45), No. 7, July 2012, pp. 2460-2477.
Elsevier DOI
1203
Classification; Heterogeneous ensembles; Diversity; Incremental
learning; Adaptive multiclassifier systems; ARTMAP neural networks;
Biometrics; Face recognition in video
BibRef
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Meynet, J.[Julien],
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Information theoretic combination of pattern classifiers,
PR(43), No. 10, October 2010, pp. 3412-3421.
Elsevier DOI
1007
Machine learning; Pattern recognition; Classifier combination;
Information theory; Mutual information; Diversity
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Zhang, L.[Li],
Zhou, W.D.[Wei-Da],
Sparse ensembles using weighted combination methods based on linear
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PR(44), No. 1, January 2011, pp. 97-106.
Elsevier DOI
1003
Classifier ensemble; Linear weighted combination; Linear programming;
Sparse ensembles; k nearest neighbor
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Montalvao, J.[Jugurta],
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Clustering ensembles and space discretization:
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PRL(31), No. 15, 1 November 2010, pp. 2415-2424.
Elsevier DOI
1003
Clustering ensembles; Weak partitions; ANMI criterion; Binary morphology
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Moshtaghi, M.[Masud],
Rajasegarar, S.[Sutharshan],
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An efficient hyperellipsoidal clustering algorithm for
resource-constrained environments,
PR(44), No. 9, September 2011, pp. 2197-2209.
Elsevier DOI
1106
HyCARCE; Data clustering; Hyperellipsoidal clustering; Wireless sensor
networks; Low computational cost clustering algorithm
BibRef
Rajasegarar, S.[Sutharshan],
Gluhak, A.[Alexander],
Imran, M.A.[Muhammad Ali],
Nati, M.[Michele],
Moshtaghi, M.[Masud],
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Palaniswami, M.[Marimuthu],
Ellipsoidal neighbourhood outlier factor for distributed anomaly
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1406
Anomaly detection
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Rokach, L.[Lior],
Pattern Classification Using Ensemble Methods,
World ScientificSingapore, 2009
ISBN: 978-981-4271-06-6
HTML Version.
Survay, Pattern Classification. Buy this book: Pattern Classification Using Ensemble Methods (Series in Machine Perception and Artificial Intelligence)
Describe classical methods and new approaches to help determine which
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BibRef
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Mimaroglu, S.[Selim],
Aksehirli, E.[Emin],
Improving DBSCAN's execution time by using a pruning technique on bit
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PRL(32), No. 13, 1 October 2011, pp. 1572-1580.
Elsevier DOI
1109
Clustering; DBSCAN; Binary methods; Pruning
BibRef
Christou, I.T.[Ioannis T.],
Coordination of Cluster Ensembles via Exact Methods,
PAMI(33), No. 2, February 2011, pp. 279-293.
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1101
Optimization cluster combinations.
BibRef
Gurrutxaga, I.[Ibai],
Muguerza, J.[Javier],
Arbelaitz, O.[Olatz],
Perez, J.M.[Jesus M.],
Martin, J.I.[Jose I.],
Towards a standard methodology to evaluate internal cluster validity
indices,
PRL(32), No. 3, 1 February 2011, pp. 505-515.
Elsevier DOI
1101
Cluster validation; Cluster validity index; Unsupervised learning
BibRef
Arbelaitz, O.[Olatz],
Gurrutxaga, I.[Ibai],
Muguerza, J.[Javier],
Pérez, J.M.[Jesús M.],
Perona, I.[Iñigo],
An extensive comparative study of cluster validity indices,
PR(46), No. 1, January 2013, pp. 243-256.
Elsevier DOI
1209
Award, Pattern Recognition, Honorable Mention. Crisp clustering; Cluster validity index; Comparative analysis
See also SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index.
BibRef
Mao, S.S.[Sha-Sha],
Jiao, L.C.,
Xiong, L.[Lin],
Gou, S.P.[Shui-Ping],
Greedy optimization classifiers ensemble based on diversity,
PR(44), No. 6, June 2011, pp. 1245-1261.
Elsevier DOI
1102
Diversity; Matching pursuit; Greedy optimization; Residual; Selective
ensemble; Kappa-error diagram
BibRef
Hernandez-Lobato, D.[Daniel],
Martinez-Munoz, G.[Gonzalo],
Suarez, A.[Alberto],
Inference on the prediction of ensembles of infinite size,
PR(44), No. 7, July 2011, pp. 1426-1434.
Elsevier DOI
1103
Classification ensembles; Classification trees; Bayesian inference;
Infinite ensembles
BibRef
Irle, A.[Albrecht],
Kauschke, J.[Jonas],
On Kleinberg's Stochastic Discrimination Procedure,
PAMI(33), No. 7, July 2011, pp. 1482-1486.
IEEE DOI
1106
See also On the Algorithmic Implementation of Stochastic Discrimination.
BibRef
Ricamato, M.T.[Maria Teresa],
Tortorella, F.[Francesco],
Partial AUC maximization in a linear combination of dichotomizers,
PR(44), No. 10-11, October-November 2011, pp. 2669-2677.
Elsevier DOI
1101
BibRef
Earlier:
AUC-based Combination of Dichotomizers: Is Whole Maximization also
Effective for Partial Maximization?,
ICPR10(73-76).
IEEE DOI
1008
Combination of classifiers; ROC analysis; Partial AUC
BibRef
Iam-On, N.[Natthakan],
Boongoen, T.[Tossapon],
Garrett, S.[Simon],
Price, C.[Chris],
A Link-Based Approach to the Cluster Ensemble Problem,
PAMI(33), No. 12, December 2011, pp. 2396-2409.
IEEE DOI
1110
Also use similarity between clusters in consensus.
BibRef
Lu, Z.W.[Zhi-Wu],
Peng, Y.X.[Yu-Xin],
Ip, H.H.S.[Horace H.S.],
Combining multiple clusterings using fast simulated annealing,
PRL(32), No. 15, 1 November 2011, pp. 1956-1961.
Elsevier DOI
1112
Clustering ensemble; Comparing clusterings; Simulated annealing
BibRef
Tian, J.[Jin],
Li, M.Q.[Min-Qiang],
Chen, F.[Fuzan],
Kou, J.[Jisong],
Coevolutionary learning of neural network ensemble for complex
classification tasks,
PR(45), No. 4, April 2012, pp. 1373-1385.
Elsevier DOI
1112
Ensemble learning; Neural network; Coevolutionary algorithm; Classification
BibRef
Meo, R.[Rosa],
Bachar, D.[Dipankar],
Ienco, D.[Dino],
LODE: A distance-based classifier built on ensembles of positive and
negative observations,
PR(45), No. 4, April 2012, pp. 1409-1425.
Elsevier DOI
1112
Data mining; Supervised learning; Concept learning; Associative classifier
BibRef
Zhang, S.H.[Shao-Hong],
Wong, H.S.[Hau-San],
Shen, Y.[Ying],
Generalized Adjusted Rand Indices for cluster ensembles,
PR(45), No. 6, June 2012, pp. 2214-2226.
Elsevier DOI
1202
BibRef
Earlier: A1, A2, Only:
ARImp: A Generalized Adjusted Rand Index for Cluster Ensembles,
ICPR10(778-781).
IEEE DOI
1008
Cluster ensembles; Clustering evaluation; Adjusted Rand Index
BibRef
Manjunath, G.[Geetha],
Murty, M.N.[M. Narasimha],
Sitaram, D.[Dinkar],
Combining heterogeneous classifiers for relational databases,
PR(46), No. 1, January 2013, pp. 317-324.
Elsevier DOI
1209
Heterogeneous classifier; RDF; Relational data; RDBMS
BibRef
Carpineto, C.[Claudio],
Romano, G.[Giovanni],
Consensus Clustering Based on a New Probabilistic Rand Index with
Application to Subtopic Retrieval,
PAMI(34), No. 12, December 2012, pp. 2315-2326.
IEEE DOI
1210
BibRef
Jing, L.P.[Li-Ping],
Ng, M.K.,
Sparse Label-Indicator Optimization Methods for Image Classification,
IP(23), No. 3, March 2014, pp. 1002-1014.
IEEE DOI
1403
computer vision
BibRef
Sen, M.U.[Mehmet Umut],
Erdogan, H.[Hakan],
Linear classifier combination and selection using group sparse
regularization and hinge loss,
PRL(34), No. 3, 1 February 2013, pp. 265-274.
Elsevier DOI
1301
BibRef
Earlier: A2, A1:
A Unifying Framework for Learning the Linear Combiners for Classifier
Ensembles,
ICPR10(2985-2988).
IEEE DOI
1008
Classifier combination; Group sparsity; Classifier selection;
Regularized empirical risk minimization; Hinge loss
BibRef
Sagha, H.[Hesam],
Bayati, H.[Hamidreza],
del R. Millán, J.[José],
Chavarriaga, R.[Ricardo],
On-line anomaly detection and resilience in classifier ensembles,
PRL(34), No. 15, 2013, pp. 1916-1927.
Elsevier DOI
1309
Anomalous behaviors in an ensemble of classifiers
by monitoring their decisions.
BibRef
Franek, L.[Lucas],
Jiang, X.Y.[Xiao-Yi],
Ensemble clustering by means of clustering embedding in vector spaces,
PR(47), No. 2, 2014, pp. 833-842.
Elsevier DOI
1311
Ensemble clustering
BibRef
Le Capitaine, H.[Hoel],
A unified view of class-selection with probabilistic classifiers,
PR(47), No. 2, 2014, pp. 843-853.
Elsevier DOI
1311
Reject options
BibRef
Le Capitaine, H.[Hoel],
Frélicot, C.[Carl],
A family of measures for best top-n class-selective decision rules,
PR(45), No. 1, 2012, pp. 552-562.
Elsevier DOI
1410
BibRef
Earlier:
On Selecting an Optimal Number of Clusters for Color Image Segmentation,
ICPR10(3388-3391).
IEEE DOI
1008
Reject options
BibRef
And:
An Optimum Class-Rejective Decision Rule and Its Evaluation,
ICPR10(3312-3315).
IEEE DOI
1008
BibRef
Earlier:
A Family of Cluster Validity Indexes Based on a l-Order Fuzzy OR
Operator,
SSPR08(612-621).
Springer DOI
0812
BibRef
And:
A class-selective rejection scheme based on blockwise similarity of
typicality degrees,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Zhang, C.X.[Chun-Xia],
Zhang, J.S.[Jiang-She],
Ji, N.N.[Nan-Nan],
Guo, G.[Gao],
Learning ensemble classifiers via restricted Boltzmann machines,
PRL(36), No. 1, 2014, pp. 161-170.
Elsevier DOI
1312
Ensemble classifier
BibRef
Ji, N.N.[Nan-Nan],
Zhang, J.S.[Jiang-She],
Zhang, C.X.[Chun-Xia],
Wang, L.[Lei],
Discriminative restricted Boltzmann machine for invariant pattern
recognition with linear transformations,
PRL(45), No. 1, 2014, pp. 172-180.
Elsevier DOI
1407
Discriminative restricted Boltzmann machine
BibRef
Read, J.[Jesse],
Martino, L.[Luca],
Luengo, D.[David],
Efficient monte carlo methods for multi-dimensional learning with
classifier chains,
PR(47), No. 3, 2014, pp. 1535-1546.
Elsevier DOI
1312
Classifier chains
BibRef
Read, J.[Jesse],
Martino, L.[Luca],
Olmos, P.M.[Pablo M.],
Luengo, D.[David],
Scalable multi-output label prediction: From classifier chains to
classifier trellises,
PR(48), No. 6, 2015, pp. 2096-2109.
Elsevier DOI
1503
Classifier chains
BibRef
Toh, K.A.[Kar-Ann],
Tan, G.C.[Geok-Choo],
Exploiting the relationships among several binary classifiers via
data transformation,
PR(47), No. 3, 2014, pp. 1509-1522.
Elsevier DOI
1312
Binary classification
BibRef
Jin, G.[Gaole],
Raich, R.[Raviv],
Hinge loss bound approach for surrogate supervision multi-view
learning,
PRL(37), No. 1, 2014, pp. 143-150.
Elsevier DOI
1402
Multi-view learning
BibRef
Berikov, V.[Vladimir],
Weighted ensemble of algorithms for complex data clustering,
PRL(38), No. 1, 2014, pp. 99-106.
Elsevier DOI
1402
Clustering
BibRef
Ahmed, N.[Nasir],
Jalil, A.[Abdul],
Multimode Image Clustering Using Optimal Image Descriptor,
IEICE(E97-D), No. 4, April 2014, pp. 743-751.
WWW Link.
1404
BibRef
Ahmed, N.[Nasir],
Image clustering using exponential discriminant analysis,
IET-CV(9), No. 1, 2015, pp. 1-12.
DOI Link
1504
image sampling. Regularization to handle small sample size
BibRef
Ramzi, P.[Pouria],
Samadzadegan, F.[Farhad],
Reinartz, P.[Peter],
An AdaBoost Ensemble Classifier System for Classifying Hyperspectral
Data,
PFG(2014), No. 1, February 2014, pp. 27-39.
DOI Link
1405
BibRef
Li, L.J.[Lei-Jun],
Hu, Q.H.[Qing-Hua],
Wu, X.Q.[Xiang-Qian],
Yu, D.[Daren],
Exploration of classification confidence in ensemble learning,
PR(47), No. 9, 2014, pp. 3120-3131.
Elsevier DOI
1406
Ensemble learning
BibRef
Li, L.[Lin],
Stolkin, R.[Rustam],
Jiao, L.C.[Li-Cheng],
Liu, F.[Fang],
Wang, S.[Shuang],
A compressed sensing approach for efficient ensemble learning,
PR(47), No. 10, 2014, pp. 3451-3465.
Elsevier DOI
1406
Ensemble learning
BibRef
Yu, Z.W.[Zhi-Wen],
Li, L.[Le],
Gao, Y.J.[Yun-Jun],
You, J.[Jane],
Liu, J.M.[Ji-Ming],
Wong, H.S.[Hau-San],
Han, G.Q.[Guo-Qiang],
Hybrid clustering solution selection strategy,
PR(47), No. 10, 2014, pp. 3362-3375.
Elsevier DOI
1406
Cluster ensemble
BibRef
Liu, F.,
Zhang, W.,
TOPSIS-Based Consensus Model for Group Decision-Making With
Incomplete Interval Fuzzy Preference Relations,
Cyber(44), No. 8, August 2014, pp. 1283-1294.
IEEE DOI
1407
Aggregates
BibRef
Britto, Jr., A.S.[Alceu S.],
Sabourin, R.[Robert],
Oliveira, L.E.S.[Luiz E.S.],
Dynamic selection of classifiers: A comprehensive review,
PR(47), No. 11, 2014, pp. 3665-3680.
Elsevier DOI
1407
Ensemble of classifiers
BibRef
Diao, R.,
Chao, F.,
Peng, T.,
Snooke, N.,
Shen, Q.,
Feature Selection Inspired Classifier Ensemble Reduction,
Cyber(44), No. 8, August 2014, pp. 1259-1268.
IEEE DOI
1407
Accuracy
BibRef
Tepper, M.[Mariano],
Sapiro, G.[Guillermo],
A Biclustering Framework for Consensus Problems,
SIIMS(7), No. 4, 2014, pp. 2488-2525.
DOI Link
1412
Merging solution from 2 clustering solutions.
BibRef
Mao, S.S.[Sha-Sha],
Jiao, L.C.[Li-Cheng],
Xiong, L.[Lin],
Gou, S.P.[Shui-Ping],
Chen, B.[Bo],
Yeung, S.K.[Sai-Kit],
Weighted classifier ensemble based on quadratic form,
PR(48), No. 5, 2015, pp. 1688-1706.
Elsevier DOI
1502
Ensemble learning
BibRef
Guo, Y.W.[Yu-Wei],
Jiao, L.C.[Li-Cheng],
Wang, S.[Shuang],
Wang, S.[Shuo],
Liu, F.[Fang],
Rong, K.X.[Kai-Xuan],
Xiong, T.[Tao],
A novel dynamic rough subspace based selective ensemble,
PR(48), No. 5, 2015, pp. 1638-1652.
Elsevier DOI
1502
Rough set
BibRef
Hayat, M.[Munawar],
Bennamoun, M.[Mohammed],
An, S.[Senjian],
Deep Reconstruction Models for Image Set Classification,
PAMI(37), No. 4, April 2015, pp. 713-727.
IEEE DOI
1503
BibRef
Earlier:
Learning Non-linear Reconstruction Models for Image Set
Classification,
CVPR14(1915-1922)
IEEE DOI
1409
BibRef
Earlier:
Reverse Training: An Efficient Approach for Image Set Classification,
ECCV14(VI: 784-799).
Springer DOI
1408
Data models.
Deep Learning; Face Recognition; Image Set Classification.
Binary classifier, one class from all others. Extend binary to multi-class.
See comment:
See also Ghost Numbers.
BibRef
Hayat, M.[Munawar],
Bennamoun, M.[Mohammed],
An, S.[Senjian],
Response to 'Ghost Numbers',
PAMI(40), No. 10, October 2018, pp. 2540-2540.
IEEE DOI
1809
Response to:
See also Ghost Numbers. Due to lack of details in the original paper, a misinterpretation of the
experimental settings was made in the comment.
BibRef
Shah, S.A.A.,
Nadeem, U.,
Bennamoun, M.[Mohammed],
Sohel, F.A.,
Togneri, R.,
Efficient Image Set Classification Using Linear Regression Based
Image Reconstruction,
Biometrics17(601-610)
IEEE DOI
1709
Euclidean distance, Image recognition, Image reconstruction,
Image resolution, Machine learning, Training
BibRef
Chen, L.[Liang],
Casperson, D.[David],
Gao, L.X.[Li-Xin],
Ghost Numbers,
PAMI(40), No. 10, October 2018, pp. 2538-2539.
IEEE DOI
1809
Comment on:
See also Deep Reconstruction Models for Image Set Classification. Claims there are problems.
See reply:
See also Response to Ghost Numbers.
BibRef
Zhong, C.M.[Cai-Ming],
Yue, X.D.[Xiao-Dong],
Zhang, Z.[Zehua],
Lei, J.S.[Jing-Sheng],
A clustering ensemble:
Two-level-refined co-association matrix with path-based transformation,
PR(48), No. 8, 2015, pp. 2699-2709.
Elsevier DOI
1505
Clustering
BibRef
Nguyen, T.T.[Tien Thanh],
Nguyen, T.T.T.[Thi Thu Thuy],
Pham, X.C.[Xuan Cuong],
Liew, A.W.C.[Alan Wee-Chung],
A novel combining classifier method based on Variational Inference,
PR(49), No. 1, 2016, pp. 198-212.
Elsevier DOI
1511
Ensemble method
BibRef
Younsi, R.[Reda],
Bagnall, A.[Anthony],
Ensembles of random sphere cover classifiers,
PR(49), No. 1, 2016, pp. 213-225.
Elsevier DOI
1511
Sphere cover
BibRef
Gutierrez, P.[Patricia],
Osman, N.[Nardine],
Roig, C.[Carme],
Sierra, C.[Carles],
Trust-based community assessment,
PRL(67, Part 1), No. 1, 2015, pp. 49-58.
Elsevier DOI
1511
Trust
BibRef
Huang, D.[Dong],
Lai, J.H.[Jian-Huang],
Wang, C.D.[Chang-Dong],
Ensemble clustering using factor graph,
PR(50), No. 1, 2016, pp. 131-142.
Elsevier DOI
1512
Ensemble clustering
BibRef
Huang, D.[Dong],
Wang, C.D.[Chang-Dong],
Lai, J.H.[Jian-Huang],
Locally Weighted Ensemble Clustering,
Cyber(48), No. 5, May 2018, pp. 1460-1473.
IEEE DOI
1804
Clustering algorithms, Estimation, Indexes,
Partitioning algorithms, Robustness, Uncertainty,
weighted-ensemble-clustering
BibRef
Xu, Y.M.[Yu-Meng],
Wang, C.D.[Chang-Dong],
Lai, J.H.[Jian-Huang],
Weighted Multi-view Clustering with Feature Selection,
PR(53), No. 1, 2016, pp. 25-35.
Elsevier DOI
1602
Data clustering
BibRef
Kim, K.[Kyounghoon],
Lin, H.[Helin],
Choi, J.Y.[Jin Young],
Choi, K.Y.[Ki-Young],
A design framework for hierarchical ensemble of multiple feature
extractors and multiple classifiers,
PR(52), No. 1, 2016, pp. 1-16.
Elsevier DOI
1601
Ensemble of detection systems
BibRef
He, L.[Li],
Zhang, H.[Hong],
Iterative ensemble normalized cuts,
PR(52), No. 1, 2016, pp. 274-286.
Elsevier DOI
1601
Iterative ensemble NCut
BibRef
Fijalkow, I.,
Heiman, E.,
Messer, H.,
Parameter Estimation from Heterogeneous/Multimodal Data Sets,
SPLetters(23), No. 3, March 2016, pp. 390-393.
IEEE DOI
1603
distributed sensors
BibRef
Shoari, A.,
Mateos, G.,
Seyedi, A.,
Analysis of Target Localization With Ideal Binary Detectors via
Likelihood Function Smoothing,
SPLetters(23), No. 5, May 2016, pp. 737-741.
IEEE DOI
1604
Approximation error
BibRef
Shoari, A.,
Mateos, G.,
On the Definition and Existence of a Minimum Variance Unbiased
Estimator for Target Localization,
SPLetters(23), No. 7, July 2016, pp. 964-968.
IEEE DOI
1608
signal detection
BibRef
Shoari, A.,
Seyedi, A.,
On Localization of A Non-Cooperative Target with Non-Coherent Binary
Detectors,
SPLetters(21), No. 6, June 2014, pp. 746-750.
IEEE DOI
1404
Acoustic measurements
BibRef
Cavalcanti, G.D.C.[George D.C.],
Oliveira, L.S.[Luiz S.],
Moura, T.J.M.[Thiago J.M.],
Carvalho, G.V.[Guilherme V.],
Combining diversity measures for ensemble pruning,
PRL(74), No. 1, 2016, pp. 38-45.
Elsevier DOI
1604
Ensemble pruning
BibRef
Bhardwaj, M.[Manju],
Bhatnagar, V.[Vasudha],
Sharma, K.[Kapil],
Cost-effectiveness of classification ensembles,
PR(57), No. 1, 2016, pp. 84-96.
Elsevier DOI
1605
Ensemble
BibRef
Santhanam, V.[Venkataraman],
Morariu, V.I.[Vlad I.],
Harwood, D.[David],
Davis, L.S.[Larry S.],
A non-parametric approach to extending generic binary classifiers for
multi-classification,
PR(58), No. 1, 2016, pp. 149-158.
Elsevier DOI
1606
Multi-classification
BibRef
Chen, J.[Jike],
Xia, J.[Junshi],
Du, P.J.[Pei-Jun],
Chanussot, J.[Jocelyn],
Xue, Z.H.[Zhao-Hui],
Xie, X.J.[Xiang-Jian],
Kernel Supervised Ensemble Classifier for the Classification of
Hyperspectral Data Using Few Labeled Samples,
RS(8), No. 7, 2016, pp. 601.
DOI Link
1608
BibRef
Gan, L.[Le],
Xia, J.S.[Jun-Shi],
Du, P.J.[Pei-Jun],
Chanussot, J.[Jocelyn],
Multiple Feature Kernel Sparse Representation Classifier for
Hyperspectral Imagery,
GeoRS(56), No. 9, September 2018, pp. 5343-5356.
IEEE DOI
1809
Kernel, Feature extraction, Shape, Dictionaries,
Hyperspectral imaging, Task analysis,
sparse representation
BibRef
Lin, L.[Liang],
Corso, J.J.[Jason J.],
Zuo, W.M.[Wang-Meng],
Zhang, D.[David],
Yao, B.Z.[Benjamin Z.],
Compositional models and Structured learning for visual recognition,
PR(59), No. 1, 2016, pp. 1-4.
Elsevier DOI
1609
BibRef
Yu, Z.W.[Zhi-Wen],
Wang, D.X.[Da-Xing],
You, J.[Jane],
Wong, H.S.[Hau-San],
Wu, S.[Si],
Zhang, J.[Jun],
Han, G.Q.[Guo-Qiang],
Progressive subspace ensemble learning,
PR(60), No. 1, 2016, pp. 692-705.
Elsevier DOI
1609
Ensemble learning
BibRef
Lourenço, P.[Pedro],
Guerreiro, B.J.[Bruno J.],
Ponti, M.[Moacir],
Kittler, J.V.[Josef V.],
Riva, M.[Mateus],
de Campos, T.[Teófilo],
Zor, C.[Cemre],
A decision cognizant Kullback-Leibler divergence,
PR(61), No. 1, 2017, pp. 470-478.
Elsevier DOI
1705
Kullback-Leibler divergence
BibRef
Zhao, Z.Q.[Zhi-Qiang],
Jiao, L.C.[Li-Cheng],
Zhao, J.Q.[Jia-Qi],
Gu, J.[Jing],
Zhao, J.[Jin],
Discriminant Deep Belief Network for High-Resolution SAR Image
Classification,
PR(61), No. 1, 2017, pp. 686-701.
Elsevier DOI
1705
Discriminant feature learning
BibRef
Zhao, Z.Q.[Zhi-Qiang],
Guo, L.[Lei],
Jia, M.[Meng],
Wang, L.[Lei],
The Generalized Gamma-DBN for High-Resolution SAR Image
Classification,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link
1806
BibRef
Zhao, Z.Q.[Zhi-Qiang],
Jia, M.[Meng],
Wang, L.[Lei],
High-Resolution SAR Image Classification via Multiscale Local Fisher
Patterns,
GeoRS(59), No. 12, December 2021, pp. 10161-10178.
IEEE DOI
2112
Radar polarimetry, Feature extraction, Synthetic aperture radar,
Mixture models, Training, Task analysis, Spatial resolution,
multiscale local Fisher pattern (MLFP)
BibRef
Denitto, M.[Matteo],
Farinelli, A.[Alessandro],
Figueiredo, M.A.T.,
Bicego, M.[Manuele],
A biclustering approach based on factor graphs and the max-sum
algorithm,
PR(62), No. 1, 2017, pp. 114-124.
Elsevier DOI
1705
Biclustering
BibRef
Denitto, M.[Matteo],
Bicego, M.[Manuele],
Farinelli, A.[Alessandro],
Figueiredo, M.A.T.,
Spike and slab biclustering,
PR(72), No. 1, 2017, pp. 186-195.
Elsevier DOI
1708
Biclustering
BibRef
Denitto, M.[Matteo],
Bicego, M.[Manuele],
Farinelli, A.[Alessandro],
Pelillo, M.[Marcello],
Dominant Set Biclustering,
EMMCVPR17(49-61).
Springer DOI
1805
BibRef
Magri, L.[Luca],
Fusiello, A.[Andrea],
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage,
CVPR19(7452-7460).
IEEE DOI
2002
BibRef
Denitto, M.[Matteo],
Magri, L.[Luca],
Farinelli, A.[Alessandro],
Fusiello, A.[Andrea],
Bicego, M.[Manuele],
Multiple Structure Recovery via Probabilistic Biclustering,
SSSPR16(274-284).
Springer DOI
1611
BibRef
Denitto, M.[Matteo],
Farinelli, A.[Alessandro],
Franco, G.[Giuditta],
Bicego, M.[Manuele],
A Binary Factor Graph Model for Biclustering,
SSSPR14(394-403).
Springer DOI
1408
BibRef
Berikov, V.[Vladimir],
Pestunov, I.[Igor],
Ensemble clustering based on weighted co-association matrices: Error
bound and convergence properties,
PR(63), No. 1, 2017, pp. 427-436.
Elsevier DOI
1612
Weighted clustering ensemble
BibRef
Pourtaheri, Z.K.[Zeinab Khatoun],
Zahiri, S.H.[Seyed Hamid],
Razavi, S.M.[Seyed Mohammad],
Design and Stability Analysis of Multi-Objective Ensemble Classifiers,
ELCVIA(15), No. 1, 2016, pp. 32-47.
DOI Link
1702
BibRef
Shahraki, N.S.,
Zahiri, S.H.,
Inclined planes optimization algorithm in optimal architecture of MLP
neural networks,
IPRIA17(189-194)
IEEE DOI
1712
learning (artificial intelligence), multilayer perceptrons,
neural net architecture, optimisation, pattern classification,
the architecture of neural network
BibRef
Tian, C.W.[Chun-Wei],
Sun, G.L.[Guang-Lu],
Zhang, Q.[Qi],
Wang, W.B.[Wei-Bing],
Chen, T.[Teng],
Sun, Y.[Yuan],
Integrating Sparse and Collaborative Representation Classifications
for Image Classification,
IJIG(17), No. 02, 2017, pp. 1750007.
DOI Link
1704
BibRef
Zhao, X.W.[Xing-Wang],
Liang, J.[Jiye],
Dang, C.Y.[Chuang-Yin],
Clustering ensemble selection for categorical data based on internal
validity indices,
PR(69), No. 1, 2017, pp. 150-168.
Elsevier DOI
1706
Clustering, ensemble, selection
BibRef
Santucci, E.[Enrica],
Didaci, L.[Luca],
Fumera, G.[Giorgio],
Roli, F.[Fabio],
A parameter randomization approach for constructing classifier
ensembles,
PR(69), No. 1, 2017, pp. 1-13.
Elsevier DOI
1706
Multiple, classifier, systems
BibRef
Sevilla-Villanueva, B.[Beatriz],
Gibert, K.[Karina],
Sànchez-Marrè, M.[Miquel],
A methodology to discover and understand complex patterns:
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Multiview Clustering
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1707
Consensus, clustering
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Cluster ensembles, Consensus clustering, Mean partition,
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Ensemble of classifiers
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Ensemble of classifiers, Dynamic ensemble selection,
Classifier competence, Prototype selection
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Entropy based probabilistic collaborative clustering,
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1708
Collaborative clustering
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1801
Classifier ensembles
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1801
Multiple classifier systems
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Classifier Pool Generation based on a Two-level Diversity Approach,
ICPR21(2414-2421)
IEEE DOI
2105
Protocols, Evolutionary computation,
Extraterrestrial measurements, Complexity theory, Optimization
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Multinomial classification
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Bilevel Multiview Latent Space Learning,
CirSysVideo(28), No. 2, February 2018, pp. 327-341.
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1802
Feature extraction, Kernel, Manifolds, Optimization, Robustness,
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Multiview Label Sharing for Visual Representations and
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1804
Combine results from different views.
Convolutional codes, Correlation, Encoding, Neural networks,
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DBSCAN, -Approximate DBSCAN, NQ-DBSCAN
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1901
Mixture model, Mixed feature, Cluster analysis, Comorbidity,
Generalised Bernoulli distribution
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Adaptive Semi-Supervised Classifier Ensemble for High Dimensional
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IEEE DOI
1901
Feature extraction, Power capacitors, Semisupervised learning,
Training, Laplace equations, Robustness, Classification,
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Nguyen, T.T.,
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Aggregation of Classifiers: A Justifiable Information Granularity
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Cyber(49), No. 6, June 2019, pp. 2168-2177.
IEEE DOI
1904
Training, Uncertainty, Prediction algorithms, Upper bound,
Cybernetics, Bagging, Cognition, Ensemble method,
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Ensemble clustering based on evidence extracted from the
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1905
Clustering ensemble, Co-association matrix, Path-based distance
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Unsupervised Ensemble Classification With Correlated Decision Agents,
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1906
Signal processing algorithms, Training data,
Maximum likelihood estimation, Correlation, Feature extraction,
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1909
Multiple classifier system, Ensemble learning,
Attentional mechanism, Diversity-based learning
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Ensemble method, Multiple classifier system,
Ensemble selection, Classifier selection, Artificial bee colony
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Probabilistic SVM, Group method of data handling,
Ensemble selection, Regularity criterion, Borda sorting
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Multiple strong and balanced cluster-based ensemble of deep learners,
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Deep learning, Ensemble classifier, Neural networks, Clustering
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2009
Multi-label learning, -Labelsets Ensemble, Label powerset, Separability
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Chen, Y.W.[Ye-Wang],
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Bouguila, N.[Nizar],
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BLOCK-DBSCAN: Fast clustering for large scale data,
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DBSCAN, -approximate DBSCAN, BLOCK-DBSCAN, Core block
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Stochastic Fixed Point Optimization Algorithm for Classifier Ensemble,
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IEEE DOI
2009
Optimization, Convergence, Machine learning algorithms,
Approximation algorithms, Classification algorithms,
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A lower bound for generalized median based consensus learning using
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2012
Consensus learning, Generalized median, Kernel function, Lower bound
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2102
Data streams, Heterogeneous ensembles, Ensemble selection
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Cao, Y.H.[Yun-Hao],
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Neural random subspace,
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Elsevier DOI
2102
Random subspace, Ensemble learning, Deep neural networks
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Muti-modal learning in photogrammetry and remote sensing,
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2109
The problem of selecting an appropriate
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Clustering, Theory, Properties
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Mixability of integral losses: A key to efficient online aggregation
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2109
Integral loss functions, Mixability, Exponential concavity,
Prediction with expert advice, Functional forecasting,
Probabilistic forecasting
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Online aggregation of probability forecasts with confidence,
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Elsevier DOI
2109
On-line learning, Prediction with expert advice,
Aggregating algorithm, Probabilistic prediction,
Smooth confidence levels for experts
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Thiagarajan, K.[Kowsalya],
Anandan, M.M.[Mukunthan Manapakkam],
Stateczny, A.[Andrzej],
Divakarachari, P.B.[Parameshachari Bidare],
Lingappa, H.K.[Hemalatha Kivudujogappa],
Satellite Image Classification Using a Hierarchical Ensemble Learning
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RS(13), No. 21, 2021, pp. xx-yy.
DOI Link
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Lin, H.[Hai],
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Ensemble cross-stage partial attention network for image
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IET-IPR(16), No. 1, 2022, pp. 102-112.
DOI Link
2112
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Ramdane, L.F.H.C.[Lamia Fatma Houbaba Chaouche],
Mahi, H.[Habib],
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Multiple classifier system for remotely sensed data clustering,
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Weighted clustering ensemble: A review,
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Elsevier DOI
2203
Ensemble selection, Fuzzy clustering, Labeling correspondence,
Multi-view data, Temporal data
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An analysis of heuristic metrics for classifier ensemble pruning
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Elsevier DOI
2203
Heuristic optimization, Ensemble selection, Ensemble pruning,
Classifier ensemble, Machine learning, Difficult samples,
Classifier complementariness
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Wang, H.C.[Hsin-Chieh],
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Lin, C.W.[Chia-Wen],
Ensemble Learning With Manifold-Based Data Splitting for Noisy Label
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MultMed(24), 2022, pp. 1127-1140.
IEEE DOI
2203
Noise measurement, Manifolds, Training, Prototypes, Data models,
Task analysis, Propagation losses, Ensemble learning, label noise,
label correction
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Xia, X.B.[Xiao-Bo],
Wang, R.[Ruxin],
Li, J.T.[Jia-Tong],
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Liu, T.L.[Tong-Liang],
LR-SVM+: Learning Using Privileged Information with Noisy Labels,
MultMed(24), 2022, pp. 1080-1092.
IEEE DOI
2203
Noise measurement, Support vector machines, Training, Optimization,
Robustness, Linear programming, Task analysis, SVM+,
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Liu, H.F.[Hua-Feng],
Wang, Q.[Qiong],
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Tang, Z.M.[Zhen-Min],
Co-LDL: A Co-Training-Based Label Distribution Learning Method for
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MultMed(24), 2022, pp. 1093-1104.
IEEE DOI
2203
Training, Noise measurement, Deep learning, Training data,
Neural networks, Task analysis, Label noise, co-training,
self-supervised representation learning
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Kalnishkan, Y.[Yuri],
Prediction with expert advice for a finite number of experts:
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PR(126), 2022, pp. 108557.
Elsevier DOI
2204
Online learning, Prediction, Model selection
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Kuchibhotla, A.K.[Arun K.],
Ramdas, A.[Aaditya],
Nested conformal prediction and quantile out-of-bag ensemble methods,
PR(127), 2022, pp. 108496.
Elsevier DOI
2205
Conformal prediction, Quantile regression, Cross-conformal,
Out-of-bag methods, Ensemble methods, Random forests
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Wang, W.J.[Wen-Ju],
Zhou, H.R.[Hao-Ran],
Chen, G.[Gang],
Wang, X.L.[Xiao-Lin],
Fusion of a Static and Dynamic Convolutional Neural Network for
Multiview 3D Point Cloud Classification,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
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Wu, T.Y.[Tian-Yi],
Tang, S.[Sheng],
Zhang, R.[Rui],
Guo, G.D.[Guo-Dong],
Consensus Feature Network for Scene Parsing,
MultMed(24), 2022, pp. 3208-3217.
IEEE DOI
2207
Assign one of the semantic categories to each pixel.
Transforms, Semantics, Convolution, Feature extraction, Training,
Network architecture, Information and communication technology,
Category Consensus Transform
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Huang, Q.[Qirui],
Gao, R.[Rui],
Akhavan, H.[Hoda],
An ensemble hierarchical clustering algorithm based on merits at
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PR(136), 2023, pp. 109255.
Elsevier DOI
2301
Ensemble clustering, Cluster consensus, Hyper-cluster,
Merit level, Robustness measure
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Oikonomidis, A.[Alexandros],
Pegia, M.[Maria],
Moumtzidou, A.[Anastasia],
Gialampoukidis, I.[Ilias],
Vrochidis, S.[Stefanos],
Kompatsiaris, I.[Ioannis],
Fusion of Multiple Classifiers Using Self Supervised Learning for
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MMMod23(II: 623-634).
Springer DOI
2304
BibRef
Zhang, Y.Q.[You-Qiang],
Sun, J.[Jin],
Shi, H.[Hao],
Ge, Z.X.[Zi-Xian],
Yu, Q.Q.[Qi-Qiong],
Cao, G.[Guo],
Li, X.S.[Xue-Song],
Agreement and Disagreement-Based Co-Learning with Dual Network for
Hyperspectral Image Classification with Noisy Labels,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link
2306
BibRef
Rohlfs, C.[Chris],
Problem-dependent attention and effort in neural networks with
applications to image resolution and model selection,
IVC(135), 2023, pp. 104696.
Elsevier DOI
2306
Attention, Neural networks, Propensity scores,
Ensemble learning, Deep learning, Flops
BibRef
Wang, Z.Z.[Zhao-Zhi],
Su, K.F.[Ke-Fan],
Zhang, J.[Jian],
Jia, H.Z.[Hui-Zhu],
Ye, Q.X.[Qi-Xiang],
Xie, X.D.[Xiao-Dong],
Lu, Z.Q.[Zong-Qing],
Multi-Agent Automated Machine Learning,
CVPR23(11960-11969)
IEEE DOI
2309
BibRef
Yan, X.[Xing],
Su, Y.H.[Yong-Hua],
Ma, W.X.[Wen-Xuan],
Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction
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PAMI(45), No. 11, November 2023, pp. 13068-13082.
IEEE DOI
2310
BibRef
Wu, D.[Di],
Xie, Y.R.[Yu-Rong],
Qiang, Z.[Zhe],
An efficient EM algorithm for two-layer mixture model of gaussian
process functional regressions,
PR(143), 2023, pp. 109783.
Elsevier DOI
2310
Mixture of gaussian processes, Hierarchical mixture of experts,
Classification EM algorithm, Curve clustering
BibRef
Chen, J.[Jie],
Mao, H.[Hua],
Peng, D.Z.[De-Zhong],
Zhang, C.Q.[Chang-Qing],
Peng, X.[Xi],
Multiview Clustering by Consensus Spectral Rotation Fusion,
IP(32), 2023, pp. 5153-5166.
IEEE DOI
2310
BibRef
Xu, L.[Li],
Liu, J.[Jun],
Experts Collaboration Learning for Continual Multi-Modal Reasoning,
IP(32), 2023, pp. 5087-5098.
IEEE DOI
2310
BibRef
Xu, Q.[Qin],
Wang, J.[Jiahui],
Jiang, B.[Bo],
Luo, B.[Bin],
Fine-Grained Visual Classification via Internal Ensemble Learning
Transformer,
MultMed(25), 2023, pp. 9015-9028.
IEEE DOI
2312
BibRef
Dou, P.[Peng],
Huang, C.L.[Chun-Lin],
Han, W.X.[Wei-Xiao],
Hou, J.L.[Jin-Liang],
Zhang, Y.[Ying],
Gu, J.[Juan],
Remote sensing image classification using an ensemble framework
without multiple classifiers,
PandRS(208), 2024, pp. 190-209.
Elsevier DOI
2402
Deep learning, Ensemble learning, Feature relationship,
Classification, Remote sensing
BibRef
Xie, F.Y.[Fang-Yuan],
Nie, F.P.[Fei-Ping],
Yu, W.Z.[Wei-Zhong],
Li, X.L.[Xue-Long],
Parameter-free ensemble clustering with dynamic weighting mechanism,
PR(151), 2024, pp. 110389.
Elsevier DOI
2404
Parameter-free, Weighted ensemble clustering,
Dynamic weighting, Self-weighted clustering
BibRef
Li, Y.[Ying],
Li, L.L.[Lin-Lin],
Liu, X.Y.[Xiang-Yu],
Liu, Y.J.[Yi-Jun],
Li, Q.Q.[Qian-Qian],
Influence maximization for heterogeneous networks based on
self-supervised clustered heterogeneous graph transformer,
PR(154), 2024, pp. 110595.
Elsevier DOI
2406
Influence maximization, Heterogeneous network,
Clustering information, Heterogeneous graph transformer
BibRef
Alziati, M.[Michele],
Amarù, F.[Fiore],
Magri, L.[Luca],
Arrigoni, F.[Federica],
Ensemble clustering via synchronized relabelling,
PRL(184), 2024, pp. 176-182.
Elsevier DOI
2408
Ensemble clustering, Relabelling and voting, Permutation synchronization
BibRef
Zhang, X.L.[Xiao-Lei],
Li, X.L.[Xue-Long],
Robust multilayer bootstrap networks in ensemble for unsupervised
representation learning and clustering,
PR(156), 2024, pp. 110739.
Elsevier DOI
2408
Ensemble selection, Cluster ensemble,
Multilayer bootstrap networks, Unsupervised learning
BibRef
Aly, H.[Hussein],
Al-Ali, A.K.[Abdulaziz K.],
Suganthan, P.N.[Ponnuthurai Nagaratnam],
Boosted multilayer feedforward neural network with multiple output
layers,
PR(156), 2024, pp. 110740.
Elsevier DOI
2408
Multiple output layers, Layer-wise training, Boosting,
Ensemble classifier, Tabular data classification
BibRef
Togban, E.[Elvis],
Ziou, D.[Djemel],
Hierarchical mixture of discriminative Generalized Dirichlet
classifiers,
PR(156), 2024, pp. 110789.
Elsevier DOI
2408
Compositional data, Generalized Dirichlet, Hierarchical mixture of experts,
Variational approximation, Upper-bound of generalized Dirichlet mixture
BibRef
Zhang, Z.L.[Zhong-Liang],
Zhu, Y.H.[Yun-Hao],
Luo, X.G.[Xing-Gang],
DES-AS: Dynamic ensemble selection based on algorithm Shapley,
PR(157), 2025, pp. 110899.
Elsevier DOI
2409
Dynamic ensemble selection, Classifier competence,
Shapley value, Monte Carlo simulation, Dynamic weighting
BibRef
Zhao, Y.[Yue],
Shen, Y.[Yantao],
Xiong, Y.J.[Yuan-Jun],
Yang, S.[Shuo],
Xia, W.[Wei],
Tu, Z.W.[Zhuo-Wen],
Schiele, B.[Bernt],
Soatto, S.[Stefano],
Elodi: Ensemble Logit Difference Inhibition for Positive-Congruent
Training,
PAMI(46), No. 12, December 2024, pp. 7529-7541.
IEEE DOI
2411
Data models, Training, Predictive models, Costs,
Computational modeling, Error analysis, ensemble learning
BibRef
Ma, K.[Ke],
Xu, Q.Q.[Qian-Qian],
Zeng, J.S.[Jin-Shan],
Liu, W.[Wei],
Cao, X.C.[Xiao-Chun],
Sun, Y.[Yingfei],
Huang, Q.M.[Qing-Ming],
Sequential Manipulation Against Rank Aggregation:
Theory and Algorithm,
PAMI(46), No. 12, December 2024, pp. 9353-9370.
IEEE DOI
2411
Data collection, Soft sensors, Games, Uncertainty, Sports, Costs,
Bayes methods, Online manipulation, adversarial learning,
ranking aggregation
BibRef
He, Z.X.[Zhen-Xin],
Li, G.X.[Guo-Xu],
Wang, Z.[Zheng],
He, G.X.[Guan-Xiong],
Yan, H.[Hao],
Wang, R.[Rong],
Deep Ensemble Remote Sensing Scene Classification via Category
Distribution Association,
RS(16), No. 21, 2024, pp. 4084.
DOI Link
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Zhang, Z.T.[Zi-Tong],
Chen, X.J.[Xiao-Jun],
Wang, C.[Chen],
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Song, W.[Wei],
Nie, F.P.[Fei-Ping],
A Structured Bipartite Graph Learning method for ensemble clustering,
PR(160), 2025, pp. 111133.
Elsevier DOI
2501
Clustering, Ensemble clustering, Structure learning
BibRef
Jindal, A.[Akshit],
Goyal, V.[Vikram],
Anand, S.[Saket],
Arora, C.[Chetan],
Army of Thieves:
Enhancing Black-Box Model Extraction via Ensemble based sample selection,
WACV24(3811-3820)
IEEE DOI Code:
WWW Link.
2404
MIMICs, Closed box, Training data, Semisupervised learning,
Streaming media, Data models, Noise measurement, Algorithms,
ethical computer vision
BibRef
Wang, Y.Z.[Yuan-Zhi],
Cui, Z.[Zhen],
Li, Y.[Yong],
Distribution-Consistent Modal Recovering for Incomplete Multimodal
Learning,
ICCV23(21968-21977)
IEEE DOI Code:
WWW Link.
2401
BibRef
Valdenegro-Toro, M.[Matias],
Sub-Ensembles for Fast Uncertainty Estimation in Neural Networks,
LXCV-ICCV23(4121-4129)
IEEE DOI
2401
BibRef
Xia, G.X.[Guo-Xuan],
Bouganis, C.S.[Christos-Savvas],
Window-Based Early-Exit Cascades for Uncertainty Estimation: When
Deep Ensembles are More Efficient than Single Models,
ICCV23(17322-17334)
IEEE DOI Code:
WWW Link.
2401
BibRef
Vimal, K.B.,
Bachu, S.[Saketh],
Garg, T.[Tanmay],
Narasimhan, N.L.[Niveditha Lakshmi],
Konuru, R.[Raghavan],
Balasubramanian, V.N.[Vineeth N.],
Building a Winning Team: Selecting Source Model Ensembles using a
Submodular Transferability Estimation Approach,
ICCV23(11575-11586)
IEEE DOI
2401
BibRef
Meding, I.[Isak],
Bodin, A.[Alexander],
Tonderski, A.[Adam],
Johnander, J.[Joakim],
Petersson, C.[Christoffer],
Svensson, L.[Lennart],
You can have your ensemble and run it too: Deep Ensembles Spread Over
Time,
BRAVO23(4022-4031)
IEEE DOI
2401
BibRef
Fu, H.J.[Hai-Jie],
Yue, X.D.[Xiao-Dong],
Liu, W.[Wei],
Denoeux, T.[Thierry],
Stable Clustering Ensemble Based on Evidence Theory,
ICIP22(2046-2050)
IEEE DOI
2211
Image segmentation, Image analysis, Evidence theory,
Measurement uncertainty, Clustering algorithms, evidence theory
BibRef
Haghighi, F.[Fatemeh],
Taher, M.R.H.[Mohammad Reza Hosseinzadeh],
Gotway, M.B.[Michael B.],
Liang, J.M.[Jian-Ming],
DiRA: Discriminative, Restorative, and Adversarial Learning for
Self-supervised Medical Image Analysis,
CVPR22(20792-20802)
IEEE DOI
2210
WWW Link. Unite discriminative, restorative, and adversarial learning.
Representation learning, Visualization, Annotations, Semantics,
Self-supervised learning, Adversarial machine learning, Medical,
Self- semi- meta- unsupervised learning
BibRef
Gwilliam, M.[Matthew],
Shrivastava, A.[Abhinav],
Beyond Supervised vs. Unsupervised: Representative Benchmarking and
Analysis of Image Representation Learning,
CVPR22(9632-9642)
IEEE DOI
2210
Representation learning, Measurement, Current measurement,
Benchmark testing, Image representation,
Self- semi- meta- Representation learning
BibRef
Shanmugam, D.[Divya],
Blalock, D.[Davis],
Balakrishnan, G.[Guha],
Guttag, J.[John],
Better Aggregation in Test-Time Augmentation,
ICCV21(1194-1203)
IEEE DOI
2203
Test-time augmentation—the aggregation of predictions across
transformed versions of a test input.
Learning systems, Computational modeling, Buildings,
Image classification, Recognition and classification,
and ethics in vision
BibRef
Wu, Y.Z.[Yan-Zhao],
Liu, L.[Ling],
Xie, Z.W.[Zhong-Wei],
Chow, K.H.[Ka-Ho],
Wei, W.Q.[Wen-Qi],
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics,
CVPR21(16464-16472)
IEEE DOI
2111
Measurement, Correlation, Computational modeling,
Neural networks, Diversity methods, Benchmark testing
BibRef
Yang, G.[Gang],
Li, X.R.[Xi-Rong],
Classifier Belief Optimization for Visual Categorization,
MMMod21(I:567-579).
Springer DOI
2106
BibRef
Boukir, S.[Samia],
Feng, W.[Wei],
Boundary bagging to address training data issues in ensemble
classification,
ICPR21(9975-9981)
IEEE DOI
2105
Machine learning algorithms, Training data,
Classification algorithms, Bagging
BibRef
Ferreira, Á.R.[Álvaro R.],
de Rosa, G.H.[Gustavo H.],
Papa, J.P.[João P.],
Carneiro, G.[Gustavo],
Faria, F.A.[Fabio A.],
Creating Classifier Ensembles through Meta-heuristic Algorithms for
Aerial Scene Classification,
ICPR21(415-422)
IEEE DOI
2105
Image analysis, Splicing, Classification algorithms,
Convolutional neural networks, Security, Task analysis
BibRef
Yotsumata, T.,
Sakamoto, M.,
Satoh, T.,
Quality Improvement for Airborne Lidar Data Filtering Based on Deep
Learning Method,
ISPRS20(B2:355-360).
DOI Link
2012
Improve the quality of classification results when deep learning is
applied for the filtering of airborne LiDAR point cloud.
BibRef
de Blois, S.[Sébastien],
Garon, M.[Mathieu],
Gagné, C.[Christian],
Lalonde, J.F.[Jean-François],
Input Dropout for Spatially Aligned Modalities,
ICIP20(733-737)
IEEE DOI
2011
Training, Task analysis, Sensors,
Image segmentation, Detection
BibRef
Zhu, H.,
An, Z.,
Xu, K.,
Hu, X.,
Xu, Y.,
Towards More Efficient And Effective Inference:
The Joint Decision Of Multi-Participants,
ICIP20(1661-1665)
IEEE DOI
2011
Training, Convolutional neural networks,
Standards, Image edge detection, Joint Decision, Multi-Layers,
Image Classification
BibRef
Ramos, M.[Marco],
Muñoz-Jiménez, V.[Vianney],
Ramos, F.F.[Félix F.],
Learning Clasiffier Systems with Hebbian Learning for Autonomus
Behaviors,
MCPR20(328-339).
Springer DOI
2007
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Ruiz, A.[Adria],
Verbeek, J.J.[Jakob J.],
Adaptative Inference Cost With Convolutional Neural Mixture Models,
ICCV19(1872-1881)
IEEE DOI
2004
Training multiple nets at once.
convolutional neural nets, mixture models,
probability, probabilistic model, CNNs, adaptative inference cost,
Convolutional neural networks
BibRef
Zakharova, A.A.,
Podvesovskii, A.G.,
Shklyar, A.V.,
Visual and Cognitive Interpretation of Heterogeneous Data,
PTVSBB19(243-247).
DOI Link
1912
BibRef
Ostyakov, P.[Pavel],
Logacheva, E.[Elizaveta],
Suvorov, R.[Roman],
Aliev, V.[Vladimir],
Sterkin, G.[Gleb],
Khomenko, O.[Oleg],
Nikolenko, S.I.[Sergey I.],
Label Denoising with Large Ensembles of Heterogeneous Neural Networks,
Large-Scale18(IV:250-261).
Springer DOI
1905
BibRef
Labao, A.B.[Alfonso B.],
Naval, P.C.[Prospero C.],
Stabilizing Actor Policies by Approximating Advantage Distributions
from K Critics,
ICPR18(1253-1258)
IEEE DOI
1812
gradient methods, learning (artificial intelligence),
actor policies, K critics, policy gradient methods, optimal policy,
Approximation error
BibRef
Garcia, L.P.F.[Luís P. F.],
Lorena, A.C.[Ana C.],
de Souto, M.C.P.[Marcilio C. P.],
Ho, T.K.[Tin Kam],
Classifier Recommendation Using Data Complexity Measures,
ICPR18(874-879)
IEEE DOI
1812
Complexity theory, Prediction algorithms,
Support vector machines, Measurement uncertainty,
Training
BibRef
Windeatt, T.,
Optimising Ensemble of Two-Class classifiers using Spectral Analysis,
ICPR18(710-715)
IEEE DOI
1812
approximation theory, Boolean functions,
learning (artificial intelligence), pattern classification,
supervised learning
BibRef
Cai, Z.P.[Zhi-Peng],
Chin, T.J.[Tat-Jun],
Le, H.[Huu],
Suter, D.[David],
Deterministic Consensus Maximization with Biconvex Programming,
ECCV18(XII: 699-714).
Springer DOI
1810
BibRef
Lin, T.Y.[Tsung-Yu],
Maji, S.[Subhransu],
Koniusz, P.[Piotr],
Second-Order Democratic Aggregation,
ECCV18(III: 639-656).
Springer DOI
1810
BibRef
Pham, X.C.[Xuan Cuong],
Dang, M.T.[Manh Truong],
Dinh, S.V.[Sang Viet],
Hoang, S.[Son],
Nguyen, T.T.[Tien Thanh],
Liew, A.W.C.[Alan Wee-Chung],
Learning from Data Stream Based on Random Projection and Hoeffding
Tree Classifier,
DICTA17(1-8)
IEEE DOI
1804
learning (artificial intelligence), pattern classification,
trees (mathematics), Hoeffding tree classifier,
Training
BibRef
Ren, X.[Xudie],
Guo, H.N.[Hao-Nan],
Li, S.H.[Sheng-Hong],
Wang, S.L.[Shi-Lin],
Li, J.H.[Jian-Hua],
A Novel Image Classification Method with CNN-XGBoost Model,
IWDW17(378-390).
Springer DOI
1708
Combining Convolutional Neural Network (CNN) and eXtreme Gradient
Boosting (XGBoost).
BibRef
Zhao, J.,
Zhang, Z.,
Chang, Z.J.[Zhen-Jun],
Liu, D.J.[Dian-Jian],
Classifier ensemble with relevance-based feature subset selection,
ICIVC17(1137-1141)
IEEE DOI
1708
Cancer, Classification algorithms, Dermatology, Entropy, Ions,
Iris recognition, Lead, classifier ensemble,
feature subset selection, information entropy, relevance
BibRef
Tayanov, V.[Vitaliy],
Krzyzak, A.[Adam],
Suen, C.[Ching],
Classification Boosting by Data Decomposition Using Consensus-Based
Combination of Classifiers,
ICIAR17(408-415).
Springer DOI
1706
BibRef
Togban, E.[Elvis],
Ziou, D.[Djemel],
Classification Using Mixture of Discriminative Learners:
The Case of Compositional Data,
ICIAR17(416-425).
Springer DOI
1706
BibRef
Yildiz, O.T.[Olcay Taner],
Ulas, A.[Aydin],
Incremental construction of rule ensembles using classifiers produced
by different class orderings,
ICPR16(492-497)
IEEE DOI
1705
Computers, Feature extraction, Optimization,
Search problems, Technological innovation, Training,
Ensemble construction, Rule, sets
BibRef
Luan, W.T.[Wen-Tao],
Yang, Y.Z.[Ye-Zhou],
Fermüller, C.[Cornelia],
Baras, J.S.[John S.],
Reliable Attribute-Based Object Recognition Using High Predictive Value
Classifiers,
ECCV16(III: 801-815).
Springer DOI
1611
object recognition in 3D using an ensemble of attribute-based classifiers.
BibRef
Rai, N.,
Negi, S.,
Chaudhury, S.,
Deshmukh, O.,
Partial Multi-View Clustering using Graph Regularized NMF,
ICPR16(2192-2197)
IEEE DOI
1705
Algorithm design and analysis, Clustering algorithms,
Clustering methods, Laplace equations, Mathematical model, Nickel, Optimization
BibRef
Taalimi, A.[Ali],
Rahimpour, A.,
Liu, L.,
Qi, H.R.[Hai-Rong],
Multi-view task-driven recognition in visual sensor networks,
ICIP17(2099-2103)
IEEE DOI
1803
Cameras, Dictionaries, Machine learning, Optimization, Task analysis,
Training, Visualization, Band-limited Wireless Camera Network,
Task-Driven Learning
BibRef
Lad, S.,
Paredes, B.R.,
Valentin, J.,
Torr, P.H.S.[Philip H.S.],
Parikh, D.,
Knowing who to listen to: Prioritizing experts from a diverse
ensemble for attribute personalization,
ICIP16(4463-4467)
IEEE DOI
1610
Adaptation models
BibRef
Fawzi, A.[Alhussein],
Frossard, P.[Pascal],
Manitest: Are classifiers really invariant?,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Quo, L.,
Boukir, S.[Samia],
Building an ensemble classifier using ensemble margin. Application to
image classification,
ICIP17(4492-4496)
IEEE DOI
1803
Bagging, Boosting, Mathematical model, Redundancy,
Support vector machines, Training, Training data, Bagging,
multiple classifier
BibRef
Feng, W.[Wei],
Boukir, S.[Samia],
Class noise removal and correction for image classification using
ensemble margin,
ICIP15(4698-4702)
IEEE DOI
1512
Class noise removal
BibRef
Seib, V.[Viktor],
Memmesheimer, R.[Raphael],
Paulus, D.[Dietrich],
Ensemble classifier for joint object instance and category
recognition on RGB-D data,
ICIP15(143-147)
IEEE DOI
1512
category and instance recognition; ensemble classifier; mobile robots
BibRef
Chakeri, A.[Alireza],
Hall, L.O.[Lawrence O.],
Dominant Sets as a Framework for Cluster Ensembles:
An Evolutionary Game Theory Approach,
ICPR14(3457-3462)
IEEE DOI
1412
Clustering algorithms
BibRef
Dumonceaux, F.,
Raschia, G.,
Gelgon, M.,
An Algebraic Approach to Ensemble Clustering,
ICPR14(1301-1306)
IEEE DOI
1412
Aggregates; Algebra; Calculus; Context; Lattices; Semantics; Upper bound
BibRef
Yang, C.[Chun],
Yin, X.C.[Xu-Cheng],
Hao, H.W.[Hong-Wei],
Diversity-Based Ensemble with Sample Weight Learning,
ICPR14(1236-1241)
IEEE DOI
1412
Accuracy
BibRef
Bagheri, M.A.[Mohammad Ali],
Gao, Q.G.[Qi-Gang],
Escalera, S.[Sergio],
Generic Subclass Ensemble:
A Novel Approach to Ensemble Classification,
ICPR14(1254-1259)
IEEE DOI
1412
Accuracy
BibRef
Eeti, L.N.[Laxmi Narayana],
Buddhiraju, K.M.[Krishna Mohan],
Perspective Based Model for Constructing Diverse Ensemble Members in
Multi-classifier Systems for Multi-spectral Image Classification,
CIARP14(637-644).
Springer DOI
1411
BibRef
Nowozin, S.[Sebastian],
Optimal Decisions from Probabilistic Models:
The Intersection-over-Union Case,
CVPR14(548-555)
IEEE DOI
1409
decision theory; structured prediction
BibRef
Weng, C.Q.[Chao-Qun],
Wang, H.X.[Hong-Xing],
Yuan, J.S.[Jun-Song],
Learning weighted geometric pooling for image classification,
ICIP13(3805-3809)
IEEE DOI
1402
joint pooling and classification
BibRef
Yan, Y.[Yan],
Yin, X.C.[Xu-Cheng],
Wang, Z.B.[Zhi-Bin],
Yin, X.W.[Xu-Wang],
Yang, C.[Chun],
Hao, H.W.[Hong-Wei],
Sorting-Based Dynamic Classifier Ensemble Selection,
ICDAR13(673-677)
IEEE DOI
1312
learning (artificial intelligence)
BibRef
Campos, Y.[Yoisel],
Estrada, R.[Roberto],
Morell, C.[Carlos],
Ferri, F.J.[Francesc J.],
A Feature Set Decomposition Method for the Construction of
Multi-classifier Systems Trained with High-Dimensional Data,
CIARP13(I:278-285).
Springer DOI
1311
BibRef
Tu, W.T.[Wen-Ting],
Sun, S.L.[Shi-Liang],
Dynamical ensemble learning with model-friendly classifiers for domain
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ICPR12(1181-1184).
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Kovalenko, D.[Dmitry],
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On methods for incorporating evidences into posterior scoring of
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ICPR12(577-580).
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Learning Multivariate Distributions by Competitive Assembly of
Marginals,
PAMI(35), No. 2, February 2013, pp. 398-410.
IEEE DOI
1301
motivated by compositional models and Bayesian networks, for small sample
sizes.
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Matikainen, P.[Pyry],
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Classifier Ensemble Recommendation,
WebScale12(I: 209-218).
Springer DOI
1210
BibRef
Sánchez, J.[Jorge],
Redolfi, J.[Javier],
Classifier Combination Using Random Walks on the Space of Concepts,
CIARP12(789-796).
Springer DOI
1209
BibRef
Duval-Poo, M.A.[Miguel A.],
Sosa-García, J.[Joan],
Guerra-Gandón, A.[Alejandro],
Vega-Pons, S.[Sandro],
Ruiz-shulcloper, J.[José],
A New Classifier Combination Scheme Using Clustering Ensemble,
CIARP12(154-161).
Springer DOI
1209
BibRef
López, E.[Erick],
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A Machine Learning Method for High-Frequency Data Forecasting,
CIARP14(621-628).
Springer DOI
1411
BibRef
Ñanculef, R.[Ricardo],
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Allende, H.[Héctor],
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An Ensemble Method for Incremental Classification in Stationary and
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CIARP11(541-548).
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1111
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Rajasekhara, P.,
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A new clusterwise similarity for partitions based on quantitative
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ICCVGIP10(117-123).
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Get consnesus clustering.
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Cai, X.[Xiao],
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Cai, W.D.[Wei-Dong],
Huang, H.[Heng],
Heterogeneous Image Features Integration via Multi-modal
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ICCV13(1737-1744)
IEEE DOI
1403
Heterogeneous Data Integration
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Cai, X.[Xiao],
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Heterogeneous image feature integration via multi-modal spectral
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CVPR11(1977-1984).
IEEE DOI
1106
To combine the various features (SIFT, HOG, GIST, LBP, CENTRIST) using MMSC.
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Classifier Acceleration by Imitation,
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1011
Classifier Molding. Imitate arbirtary classifiers by linear regression
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Vega-Pons, S.[Sandro],
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Partition Selection Approach for Hierarchical Clustering Based on
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1011
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An Overproduce-and-Choose Strategy to Create Classifier Ensembles with
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CIARP10(500-508).
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1011
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Ensemble Clustering via Random Walker Consensus Strategy,
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Pattern Recognition Method Using Ensembles of Regularities Found by
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Classifier Selection in a Family of Polyhedron Classifiers,
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Duin, R.P.W.[Robert P. W.],
Pekkalska, E.[Elzbieta],
The Dissimilarity Representation for Structural Pattern Recognition,
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Dissimilarity-Based Classifications in Eigenspaces,
CIARP11(425-432).
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1111
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Earlier:
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CIARP10(418-425).
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1011
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Rejection Strategies with Multiple Classifiers for Handwritten
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A microeconomic approach to multi-objective spatial clustering,
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0812
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Early recognition of sequential patterns by classifier combination,
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Identification of phosphorylation sites using a hybrid classifier
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Efficient Multi-method Rule Learning for Pattern Classification Machine
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Influence of Resampling and Weighting on Diversity and Accuracy of
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Performance Analysis of Classifier Ensembles:
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New Neighborhood Based Classification Rules for Metric Spaces and Their
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A Pool of Classifiers by SLP: A Multi-class Case,
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Combining Dichotomizers for MAP Field Classification,
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A Fast and Efficient Ensemble Clustering Method,
ICPR06(II: 720-723).
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Robust Multiclass Ensemble Classifiers via Symmetric Functions,
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Benefits of Separable, Multilinear Discriminant Classification,
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0609
BibRef
And:
ICPR06(IV: 959).
IEEE DOI
0609
BibRef
Bauckhage, C.[Christian],
Kaster, T.[Thomas],
Tsotsos, J.K.[John K.],
Applying Ensembles of Multilinear Classifiers in the Frequency Domain,
CVPR06(I: 95-102).
IEEE DOI
0606
BibRef
Bauckhage, C.[Christian],
Tsotsos, J.K.[John K.],
Separable Linear Discriminant Classification,
DAGM05(318).
Springer DOI
0509
BibRef
And:
Separable Linear Classifiers for Online Learning in Appearance Based
Object Detection,
CAIP05(347).
Springer DOI
0509
BibRef
Chellapilla, K.[Kumar],
Shilman, M.[Michael],
Simard, P.Y.[Patrice Y.],
Combining Multiple Classifiers for Faster Optical Character Recognition,
DAS06(358-367).
Springer DOI
0602
BibRef
Singh, R.[Rohit],
Samal, S.[Sandeep],
Lahiri, T.[Tapobrata],
A Novel Strategy for Designing Efficient Multiple Classifier,
ICB06(713-720).
Springer DOI
0601
BibRef
Beattie, M.[Michael],
Vijaya Kumar, B.V.K.,
Lucey, S.[Simon],
Tonguz, O.K.[Ozan K.],
Combining Verification Decisions in a Multi-vendor Environment,
AVBPA05(406).
Springer DOI
0509
BibRef
Gutierrez, J.,
Rouas, J.L.,
Andre-Obrecht, R.,
Weighted loss functions to make risk-based language identification
fused decisions,
ICPR04(II: 863-866).
IEEE DOI
0409
BibRef
Yi, X.[Xing],
Kou, Z.B.[Zhong-Bao],
Zhang, C.S.[Chang-Shui],
Classifer combination based on active learning,
ICPR04(I: 184-187).
IEEE DOI
0409
BibRef
Altinçay, H.[Hakan],
Çizili, B.[Buket],
Classifier Combination through Clustering in the Output Spaces,
CAIP03(487-493).
Springer DOI
0311
BibRef
Soto, A.,
A Probabilistic Approach for the Adaptive Integration of Multiple
Visual Cues Using an Agent Framework,
CMU-RI-TR-02-30, October, 2002.
BibRef
0210
Ph.D.Thesis
HTML Version.
0306
BibRef
Sirlantzis, K.,
Fairhurst, M.C.,
Guest, R.M.,
An evolutionary algorithm for classifier and combination rule selection
in multiple classifier systems,
ICPR02(II: 771-774).
IEEE DOI
0211
BibRef
Sirlantzis, K.,
Fairhurst, M.C.,
Optimisation of Multiple Classifier Systems Using Genetic Algorithms,
ICIP01(I: 1094-1097).
IEEE DOI
0108
BibRef
And:
Investigation of a novel self-configurable multiple classifier system
for character recognition,
ICDAR01(1002-1006).
IEEE DOI
0109
BibRef
Tax, D.M.J.,
Duin, R.P.W.,
Using two-class classifiers for multiclass classification,
ICPR02(II: 124-127).
IEEE DOI
0211
BibRef
Earlier:
Data Description in Subspaces,
ICPR00(Vol II: 672-675).
IEEE DOI
0009
BibRef
Skurichina, M.,
Ypma, A.,
Duin, R.P.W.,
The Role of Subclasses in Machine Diagnostics,
ICPR00(Vol II: 668-671).
IEEE DOI
0009
BibRef
Jeong, S.H.,
Lim, K.T.,
Nam, Y.S.,
A combination method of two classifiers based on the information of
confusion matrix,
FHR02(519-523).
IEEE Top Reference.
0209
BibRef
Mahamud, S.[Shyjan],
Hebert, M.[Martial],
Lafferty, J.[John],
Combining Simple Discriminators for Object Discrimination,
ECCV02(III: 776 ff.).
Springer DOI
0205
BibRef
Qian, Y.,
Suen, C.Y.,
Clustering Combination Method,
ICPR00(Vol II: 732-735).
IEEE DOI
0009
BibRef
Mascarilla, L.,
Frélicot, C.,
Another Look at Combining Rejection-based Pattern Classifiers,
ICPR00(Vol II: 156-159).
IEEE DOI
0009
BibRef
DeCarlo, D.[Douglas],
Metaxas, D.N.[Dimitris N.],
Combining Information using Hard Constraints,
CVPR99(II: 132-138).
IEEE DOI Use hard constraints rather than statistical combination.
BibRef
9900
Kang, H.J.,
Kim, J.H.,
A Probabilistic Framework for Combining Multiple Classifiers at
Abstract Level,
ICDAR97(870-874).
IEEE DOI
9708
BibRef
Wong, G.,
Frei, H.P.,
Object recognition: the utopian method is dead; the time for combining
simple methods has come,
ICPR92(III:185-188).
IEEE DOI
9208
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Federated Learning .