14.1.5.2.1 Voting for Combinations, Classifiers

Chapter Contents (Back)
Voting. Classifer Combinations.

Windeatt, T.[Terry],
Vote counting measures for ensemble classifiers,
PR(36), No. 12, December 2003, pp. 2743-2756.
Elsevier DOI 0310
BibRef
And:
Diversity/accuracy and ensemble classifier design,
ICPR04(III: 454-457).
IEEE DOI 0409
BibRef

Windeatt, T., Ardeshir, G.,
Tree pruning for output coded ensembles,
ICPR02(II: 92-95).
IEEE DOI 0211
Convert a multi-class problem into several binary subproblems with an ensemble of binary classifiers. BibRef

Windeatt, T.[Terry], Zor, C.[Cemre],
Low Training Strength High Capacity Classifiers for Accurate Ensembles Using Walsh Coefficients,
SSSPR12(701-709).
Springer DOI 1211
BibRef

Smith, R.S.[Raymond Stuart], Windeatt, T.[Terry],
A Bias-Variance Analysis of Bootstrapped Class-Separability Weighting for Error-Correcting Output Code Ensembles,
ICPR10(61-64).
IEEE DOI 1008
BibRef

Demrekler, M.[Mübeccel], Altnçay, H.[Hakan],
Plurality Voting-Based Multiple Classifier Systems: Statistically Independent with Respect to Dependent Classifier Sets,
PR(35), No. 11, November 2002, pp. 2365-2379.
Elsevier DOI 0208
BibRef

Altnçay, H.[Hakan],
On naive Bayesian fusion of dependent classifiers,
PRL(26), No. 15, November 2005, pp. 2463-2473.
Elsevier DOI 0510
BibRef

Demir, C.[Cigdem], Alpaydin, E.[Ethem],
Cost-conscious classifier ensembles,
PRL(26), No. 14, 15 October 2005, pp. 2206-2214.
Elsevier DOI 0510
BibRef

Alpaydin, E.[Ethem],
Multiple neural networks and weighted voting,
ICPR92(II:29-32).
IEEE DOI 9208
BibRef

Tumer, K.[Kagan], Agogino, A.K.[Adrian K.],
Ensemble clustering with voting active clusters,
PRL(29), No. 14, October 2008, pp. 1947-1953.
Elsevier DOI 0804
Cluster ensembles; Consensus clustering; Distributed clustering; Adaptive clustering BibRef

Hajdu, A., Hajdu, L., Jonas, A., Kovacs, L., Toman, H.,
Generalizing the Majority Voting Scheme to Spatially Constrained Voting,
IP(22), No. 11, 2013, pp. 4182-4194.
IEEE DOI 1310
Generalized majority voting BibRef

Hajdu, A., Tomán, H., Kovács, L., Hajdu, L.,
Composing ensembles by a stochastic approach under execution time constraint,
ICPR16(222-227)
IEEE DOI 1705
Approximation algorithms, Concrete, Linear programming, Optical imaging, Optimization, Retina, Time, factors BibRef

Lv, X.W.[Xian-Wei], Ming, D.P.[Dong-Ping], Lu, T.T.[Ting-Ting], Zhou, K.[Keqi], Wang, M.[Min], Bao, H.Q.[Han-Qing],
A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Tian, T.[Tian], Zhu, J.[Jun], Qiaoben, Y.[You],
Max-Margin Majority Voting for Learning from Crowds,
PAMI(41), No. 10, October 2019, pp. 2480-2494.
IEEE DOI 1909
Task analysis, Bayes methods, Crowdsourcing, Noise measurement, Labeling, Maximum likelihood estimation, Max-margin learning, regularized Bayesian inference BibRef


Hou, H.R.[Hui-Rang], Meng, Q.H.[Qing-Hao], Zhang, X.N.[Xiao-Nei],
A Voting-Near-Extreme-Learning-Machine Classification Algorithm,
ICPR18(237-241)
IEEE DOI 1812
Training, Testing, Principal component analysis, Feature extraction, Electroencephalography, linear discriminant analysis BibRef

Rahimpour, A.[Alireza], Taalimi, A.[Ali], Luo, J.J.[Jia-Jia], Qi, H.R.[Hai-Rong],
Distributed object recognition in smart camera networks,
ICIP16(669-673)
IEEE DOI 1610
Local decisiongs, final based on majority vote. Base stations BibRef

Morvant, E.[Emilie], Habrard, A.[Amaury], Ayache, S.[Stéphane],
Majority Vote of Diverse Classifiers for Late Fusion,
SSSPR14(153-162).
Springer DOI 1408
BibRef

Orrite, C.[Carlos], Rodríguez, M.[Mario], Martínez, F.[Francisco], Fairhurst, M.[Michael],
Classifier Ensemble Generation for the Majority Vote Rule,
CIARP08(340-347).
Springer DOI 0809
BibRef

Dimililer, N.[Nazife], Varoglu, E.[Ekrem], Altonçay, H.[Hakan],
Vote-Based Classifier Selection for Biomedical NER Using Genetic Algorithms,
IbPRIA07(II: 202-209).
Springer DOI 0706
BibRef

Ekbal, A.[Asif], Bandyopadhyay, S.[Sivaji],
Improving the Performance of a NER System by Post-processing and Voting,
SSPR08(831-841).
Springer DOI 0812
BibRef

Ekbal, A.[Asif],
Improvement of Prediction Accuracy Using Discretization and Voting Classifier,
ICPR06(II: 695-698).
IEEE DOI 0609
BibRef

de Stefano, C.[Claudio], Fontanella, F.[Francesco], di Freca, A.S.[Alessandra Scotto], Marcelli, A.[Angelo],
Learning Bayesian Networks by Evolution for Classifier Combination,
ICDAR09(966-970).
IEEE DOI 0907
BibRef

de Stefano, C.[Claudio], d'Elia, C.[Ciro], Marcelli, A.[Angelo], di Freca, A.S.[Alessandra Scotto],
Using Bayesian Network for combining classifiers,
CIAP07(73-80).
IEEE DOI 0709
BibRef

de Stefano, C., della Cioppa, A., Marcelli, A.,
Exploiting reliability for dynamic selection of classifiers by means of genetic algorithms,
ICDAR03(671-675).
IEEE DOI 0311
BibRef
Earlier:
An adaptive weighted majority vote rule for combining multiple classifiers,
ICPR02(II: 192-195).
IEEE DOI 0211
BibRef

Franke, J., Mandler, E.,
A comparison of two approaches for combining the votes of cooperating classifiers,
ICPR92(II:611-614).
IEEE DOI 9208
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Bagging, Combinations, Classifiers .


Last update:Jun 29, 2020 at 10:24:28