14.1.14 Classifier, Performance Evaluation, Errors, Comparisons

Chapter Contents (Back)
Evaluation, Classifiers. Comparisons.
See also Multi-View Learning, Co-Clustering.

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Elsevier DOI 0309
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Jain, A.K., Dubes, R.C., and Chen, C.C.,
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Colussi, L.,
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The Systematic-Error Detection as a Classification Problem,
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Duin, R.P.W.,
A Note on Comparing Classifiers,
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Richards, J.A.,
Classifier Performance and MAP Accuracy,
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Nyssen, E.,
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Nyssen, E.,
Evaluation of Pattern Classifiers: Applying a Monte Carlo Significance Test to the Classification Efficiency,
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San Miguel-Ayanz, J., Biging, G.S.,
Comparison of Single-Stage and Multistage Classification Approaches for Cover Type Mapping with TM and Spot Data,
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Denoeux, T.[Thierry],
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Zhou, J.Y.[Jiang-Ying], Lopresti, D.P.[Daniel P.],
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Lerner, B.[Boaz], Guterman, H.[Hugo], Aladjem, M.[Mayer], Dinsteint, I.[Its'hak], Romem, Y.[Yitzhak],
On Pattern Classification with Sammons Nonlinear Mapping: An Experimental-Study,
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See also nonlinear mapping for data structure analysis, A. BibRef

Aladjem, M.[Mayer], Dinstein, I.[Its'hak],
A multiclass extension of discriminant mappings,
ICPR92(II:101-104).
IEEE DOI 9208
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Stehman, S.V., Czaplewski, R.L.,
Design and Analysis for Thematic Map Accuracy Assessment: Fundamental Principles,
RSE(64), No. 3, June 1998, pp. 331-344. 9806
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Adams, N.M., Hand, D.J.,
Comparing classifiers when the misallocation costs are uncertain,
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Smits, P.C., Dellepiane, S.G., Schowengerdt, R.A.,
Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach,
JRS(20), No. 8, May 1999, pp. 1461. BibRef 9905

Sohn, S.Y.[So Young],
Meta Analysis of Classification Algorithms for Pattern Recognition,
PAMI(21), No. 11, November 1999, pp. 1137-1144.
IEEE DOI 9912
For sample size and dimensionality. Meta model to compare different classification algorithms. Traditional statistical, neural nets, and machine learning approaches. BibRef

Srivastava, A.N., Su, R., Weigend, A.S.,
Data Mining for Features Using Scale-Sensitive Gated Experts,
PAMI(21), No. 12, December 1999, pp. 1268-1279.
IEEE DOI 0001
Data analysis to partition complex regression surface into simpler surfaces (features).
See also Virtual Sensors: Using Data Mining Techniques to Efficiently Estimate Remote Sensing Spectra. BibRef

Andersson, A.[Arne], Davidsson, P.[Paul], Lindén, J.[Johan],
Measure-based classifier performance evaluation,
PRL(20), No. 11-13, November 1999, pp. 1165-1173. 0001
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Lim, T.S., Loh, W.Y., Shil, Y.S.,
A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms,
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Ong, S.H., Zhao, X.,
On post-clustering evaluation and modification,
PRL(21), No. 5, May 2000, pp. 365-373. 0005
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Raudys, S.J.[Sarunas J.], Saudargiene, A.[Ausra],
First-Order Tree-Type Dependence between Variables and Classification Performance,
PAMI(23), No. 2, February 2001, pp. 233-239.
IEEE DOI 0102
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Hubert-Moy, L., Cotonnec, A., Le Du, L., Chardin, A., Perez, P.,
A Comparison of Parametric Classification Procedures of Remotely Sensed Data Applied on Different Landscape Units,
RSE(75), No. 2, 2001, pp. 174-187. 0102
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Tambouratzis, G.[George],
Improving the Clustering Performance of the Scanning n-Tuple Method by Using Self-Supervised Algorithms to Introduce Subclasses,
PAMI(24), No. 6, June 2002, pp. 722-733.
IEEE DOI 0206
BibRef
Earlier:
Improving the Classification Accuracy of the Scanning N-tuple Method,
ICPR00(Vol II: 1046-1049).
IEEE DOI 0009
Extend work of:
See also Statistical Syntactic Methods for High-Performance OCR. Remove edge effects. BibRef

Liu, M.Q.[Ming-Qin], Samal, A.[Ashok],
Cluster validation using legacy delineations,
IVC(20), No. 7, May 2002, pp. 459-467.
Elsevier DOI 0206
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Berikov, V.B.[Vladimir B.],
An approach to the evaluation of the performance of a discrete classifier,
PRL(23), No. 1-3, January 2002, pp. 227-233.
Elsevier DOI 0201
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Harvey, N.R., Theiler, J., Brumby, S.P., Perkins, S., Szymanski, J.J., Bloch, J.J., Porter, R.B., Galassi, M., Young, A.C.,
Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction,
GeoRS(40), No. 2, February 2002, pp. 393-404.
IEEE Top Reference. 0205
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Muchoney, D.M.[Douglas M.], Strahler, A.H.[Alan H.],
Pixel- and site-based calibration and validation methods for evaluating supervised classification of remotely sensed data,
RSE(81), No. 2-3, August 2002, pp. 290-299.
HTML Version. 0206
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Alsing, S.G.[Stephen G.], Bauer, Jr., K.W.[Kenneth W.], Miller, J.O.[John O.],
A multinomial selection procedure for evaluating pattern recognition algorithms,
PR(35), No. 11, November 2002, pp. 2397-2412.
Elsevier DOI 0208
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Maulik, U.[Ujjwal], Bandyopadhyay, S.[Sanghamitra],
Performance Evaluation of Some Clustering Algorithms and Validity Indices,
PAMI(24), No. 12, December 2002, pp. 1650-1654.
IEEE Abstract. 0212
Hard K-Means, Single Linkage, Simulated annealing
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Sandri, L.[Laura], Marzocchi, W.[Warner],
Testing the performance of some nonparametric pattern recognition algorithms in realistic cases,
PR(37), No. 3, March 2004, pp. 447-461.
Elsevier DOI 0401
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Toh, K.A.[Kar-Ann], Tran, Q.L.[Quoc-Long], Srinivasan, D.[Dipti],
Benchmarking a Reduced Multivariate Polynomial Pattern Classifier,
PAMI(26), No. 6, June 2004, pp. 740-755.
IEEE Abstract. 0404
The simplified model worked well. Analyze it. BibRef

Tran, Q.L.[Quoc-Long], Toh, K.A.[Kar-Ann], Srinivasan, D.[Dipti], Wong, K.L., Low, S.Q.C.[Shaun Qiu-Cen],
An empirical comparison of nine pattern classifiers,
SMC-B(35), No. 5, October 2005, pp. 1079-1091.
IEEE DOI 0510
Algorithm RM. Reduced Multivariate. BibRef

Attoor, S.N.[Sanju N.], Dougherty, E.R.[Edward R.],
Classifier performance as a function of distributional complexity,
PR(37), No. 8, August 2004, pp. 1641-1651.
Elsevier DOI 0407
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Kim, D.W.[Dae-Won], Lee, K.Y.[Ki Young], Lee, D.[Doheon], Lee, K.H.[Kwang H.],
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PR(38), No. 4, April 2005, pp. 607-611.
Elsevier DOI 0501
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Kim, D.W.[Dae-Won], Lee, K.Y.[Ki-Young], Lee, D.[Doheon], Lee, K.H.[Kwang H.],
A k-populations algorithm for clustering categorical data,
PR(38), No. 7, July 2005, pp. 1131-1134.
Elsevier DOI 0505
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Stein, A., Aryal, J., Gort, G.,
Use of the Bradley-Terry Model to Quantify Association in Remotely Sensed Images,
GeoRS(43), No. 4, April 2005, pp. 852-856.
IEEE Abstract. 0501
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Caulfield, H.J.[H. John], Heidary, K.[Kaveh],
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PR(38), No. 8, August 2005, pp. 1225-1238.
Elsevier DOI 0505
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Salman, A.[Ayed], Omran, M.G.[Mahamed G.], Engelbrecht, A.P.[Andries P.],
SIGT: Synthetic Image Generation Tool for Clustering Algorithms,
GVIP(05), No. V2, January 2005, pp. 33-44
HTML Version. Create images to test clustering. BibRef 0501

Graaff, A.J., Engelbrecht, A.P.[Andries P.],
Clustering data in an uncertain environment using an artificial immune system,
PRL(32), No. 2, 15 January 2011, pp. 342-351.
Elsevier DOI 1101
Uncertain environments; Non-stationary data; Immune networks; Clustering performance measures BibRef

Yousef, W.A.[Waleed A.], Wagner, R.F.[Robert F.], Loew, M.H.[Murray H.],
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PRL(26), No. 16, December 2005, pp. 2600-2610.
Elsevier DOI 0512
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Yousef, W.A.[Waleed A.], Wagner, R.F.[Robert F.], Loew, M.H.[Murray H.],
Assessing Classifiers from Two Independent Data Sets Using ROC Analysis: A Nonparametric Approach,
PAMI(28), No. 11, November 2006, pp. 1809-1817.
IEEE DOI 0609
BibRef
Earlier:
Comparison of non-parametric methods for assessing classifier performance in terms of ROC parameters,
AIPR04(190-195).
IEEE DOI 0410
3 Parameters: Conditional (an particular training set) AUC (area under RO Curve), mean and variance of AUC. BibRef

Baraldi, A., Bruzzone, L., Blonda, P., Carlin, L.,
Badly Posed Classification of Remotely Sensed Images: An Experimental Comparison of Existing Data Labeling Systems,
GeoRS(44), No. 1, January 2006, pp. 214-235.
IEEE DOI 0601
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Baraldi, A., Bruzzone, L., Blonda, P.,
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IP(15), No. 8, August 2006, pp. 2208-2225.
IEEE DOI 0606
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Fawcett, T.[Tom],
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PRL(27), No. 8, June 2006, pp. 861-874.
Elsevier DOI 0605
Survey, ROC Analysis. Classifier evaluation; Evaluation metrics BibRef

Stathakis, D., Vasilakos, A.,
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GeoRS(44), No. 8, August 2006, pp. 2305-2318.
IEEE DOI 0608
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Stathakis, D.[Demetris], Kanellopoulos, I.[Ioannis],
Global Elevation Ancillary Data for Land-use Classification Using Granular Neural Networks,
PhEngRS(74), No. 1, January 2008, pp. 55-64.
WWW Link. 0803
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Stathakis, D.[Demetris], Kanellopoulos, I.[Ioannis],
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PhEngRS(74), No. 10, October 2008, pp. 1259-1266.
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Arbel, R.[Reuven], Rokach, L.[Lior],
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PRL(27), No. 14, 15 October 2006, pp. 1619-1631.
Elsevier DOI 0609
Evaluation measures; Hit-rate; Recall; Receiver operating characteristic BibRef

Nangendo, G.[Grace], Skidmore, A.K.[Andrew K.], van Oosten, H.[Henk],
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PandRS(61), No. 6, February 2007, pp. 393-404.
Elsevier DOI 0703
Forest classification; Conventional classifiers; Expert System; Classification accuracy; East Africa BibRef

An, S.J.[Sen-Jian], Liu, W.Q.[Wan-Quan], Venkatesh, S.[Svetha],
Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression,
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Elsevier DOI 0704
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Earlier:
Efficient Cross-validation of the Complete Two Stages in KFD Classifier Formulation,
ICPR06(III: 240-244).
IEEE DOI 0609
Model selection; Cross-validation; Kernel methods BibRef

An, S.[Senjian], Peursum, P.[Patrick], Liu, W.Q.[Wan-Quan], Venkatesh, S.[Svetha],
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CVPR11(1409-1416).
IEEE DOI 1106
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Efficient algorithms for subwindow search in object detection and localization,
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IEEE DOI 0906
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An, S.[Senjian], Peursum, P.[Patrick], Liu, W.Q.[Wan-Quan], Venkatesh, S.[Svetha], Chen, X.M.[Xiao-Ming],
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IEEE DOI 1006
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Pham, D.S.[Duc-Son], Venkatesh, S.[Svetha],
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IEEE DOI 0806
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Pham, D.S.[Duc-Son], Venkatesh, S.[Svetha],
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IEEE DOI 0806
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Bøcher, P.K., McCloy, K.R.,
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PhEngRS(73), No. 8, August 2007, pp. 893-904.
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Devarakota, P.R.R.[Pandu Ranga Rao], Mirbach, B.[Bruno], Ottersten, B.[Bjorn],
Reliability estimation of a statistical classifier,
PRL(29), No. 3, 1 February 2008, pp. 243-253.
Elsevier DOI 0801
Pattern classification; Local density estimation; Confidence intervals; Binomial distribution; GMMs; Pattern rejection BibRef

Sahiner, B., Chan, H.P., Hadjiiski, L.M.,
Performance Analysis of Three-Class Classifiers: Properties of a 3-D ROC Surface and the Normalized Volume Under the Surface for the Ideal Observer,
MedImg(27), No. 2, February 2008, pp. 215-227.
IEEE DOI 0802
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Volkovich, Z., Barzily, Z., Morozensky, L.,
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PR(41), No. 7, July 2008, pp. 2174-2188.
Elsevier DOI 0804
Cluster validation; Negative definite functions; Statistical model BibRef

Akhbardeh, A.[Alireza], Nikhil, Koskinen, P.E.[Perttu E.], Yli-Harja, O.[Olli],
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PRL(29), No. 8, 1 June 2008, pp. 1082-1093.
Elsevier DOI 0804
Affine look-up table; Classification; Pre-classification; Post-classification; Supervised fuzzy adaptive resonance theory (SF-ART) network Iris recognition. BibRef

Ferri, C., Hernandez-Orallo, J., Modroiu, R.,
An experimental comparison of performance measures for classification,
PRL(30), No. 1, 1 January 2009, pp. 27-38.
Elsevier DOI 0811
Classification; Performance measures; Ranking; Calibration BibRef

Lago-Fernandez, L.F.[Luis F.], Corbacho, F.[Fernando],
Normality-based validation for crisp clustering,
PR(43), No. 3, March 2010, pp. 782-795.
Elsevier DOI 1001
Crisp clustering; Cluster validation; Negentropy BibRef

Chen, J.[Jin], Zhu, X.L.[Xiao-Lin], Imura, H.[Hidefumi], Chen, X.H.[Xue-Hong],
Consistency of accuracy assessment indices for soft classification: Simulation analysis,
PandRS(65), No. 2, March 2010, pp. 156-164.
Elsevier DOI 1003
Soft classification; Accuracy assessment; Sub-pixel confusion matrix; RMSE; Consistency BibRef

Pascual, D.[Damaris], Pla, F.[Filiberto], Salvador Sánchez, J.,
Cluster validation using information stability measures,
PRL(31), No. 6, 15 April 2010, pp. 454-461.
Elsevier DOI 1004
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Cluster Stability Assessment Based on Theoretic Information Measures,
CIARP08(219-226).
Springer DOI 0809
Cluster validation; Stability index; Information theory BibRef

Valverde-Albacete, F.J.[Francisco J.], Pelaez-Moreno, C.[Carmen],
Two information-theoretic tools to assess the performance of multi-class classifiers,
PRL(31), No. 12, 1 September 2010, pp. 1665-1671.
Elsevier DOI 1008
Multi-class classifier; Confusion matrix; Contingency table; Performance measure; de Finetti diagram; Entropy triangle BibRef

Lee, Y.R.[Young-Rok], Lee, J.H.[Jeong-Hwa], Jun, C.H.[Chi-Hyuck],
Stability-based validation of bicluster solutions,
PR(44), No. 2, February 2011, pp. 252-264.
Elsevier DOI 1011
Biclustering; Validation; Stability; Resampling BibRef

Fu, X.[Xin], Shen, Q.A.[Qi-Ang],
Fuzzy complex numbers and their application for classifiers performance evaluation,
PR(44), No. 7, July 2011, pp. 1403-1417.
Elsevier DOI 1103
Fuzzy complex numbers; Performance evaluation; Feature selection; Pattern classification BibRef

Wicker, N.[Nicolas],
A note on ball segment picking related to clustering,
PRL(32), No. 5, 1 April 2011, pp. 651-655.
Elsevier DOI 1103
Density of points clustering; DPC; Ball segment picking; Curse of dimensionality; Sampling BibRef

Woloszynski, T.[Tomasz], Kurzynski, M.[Marek],
A probabilistic model of classifier competence for dynamic ensemble selection,
PR(44), No. 10-11, October-November 2011, pp. 2656-2668.
Elsevier DOI 1101
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Earlier:
A Measure of Competence Based on Randomized Reference Classifier for Dynamic Ensemble Selection,
ICPR10(4194-4197).
IEEE DOI 1008
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Earlier:
On a New Measure of Classifier Competence Applied to the Design of Multiclassifier Systems,
CIAP09(995-1004).
Springer DOI 0909
Probabilistic modelling; Classifier competence; Multiple classifier system; Beta distribution BibRef

Frasch, J.V.[Janick V.], Lodwich, A.[Aleksander], Shafait, F.[Faisal], Breuel, T.M.[Thomas M.],
A Bayes-true data generator for evaluation of supervised and unsupervised learning methods,
PRL(32), No. 11, 1 August 2011, pp. 1523-1531.
Elsevier DOI 1108
Synthetic data generation; Benchmarking; Experimental proofs BibRef

Chudzian, P.[Pawel],
Evaluation measures for kernel optimization,
PRL(33), No. 9, 1 July 2012, pp. 1108-1116.
Elsevier DOI 1202
Kernel evaluation measures; Kernel optimization; Kernel methods; Radial basis function kernel; Pattern classification Transform pattern to feature space. BibRef

Gey, S.[Servane],
Risk bounds for CART classifiers under a margin condition,
PR(45), No. 9, September 2012, pp. 3523-3534.
Elsevier DOI 1206
Classification; CART; Pruning; Margin; Risk bounds. Classification And Regression Trees (CART) BibRef

Liu, J., Zhang, Z.J., Yang, Y., Wang, M.,
Comments on 'Probabilities of false alarm and detection for the NAMF operating in Gaussian clutter',
SPLetters(19), No. 10, October 2012, pp. 671.
IEEE DOI 1209
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de França, F.O., Coelho, G.P., von Zuben, F.J.,
Predicting missing values with biclustering: A coherence-based approach,
PR(46), No. 5, May 2013, pp. 1255-1266.
Elsevier DOI 1302
Biclustering; Missing data imputation; Knowledge discovery; Quadratic programming BibRef

Richards, J.A., Kingsbury, N.G.,
Is There a Preferred Classifier for Operational Thematic Mapping?,
GeoRS(52), No. 5, May 2014, pp. 2715-2725.
IEEE DOI 1403
Classification BibRef

Liu, Q.B.[Qing-Bao], Dong, G.Z.[Guo-Zhu],
CPCQ: Contrast pattern based clustering quality index for categorical data,
PR(45), No. 4, 2012, pp. 1739-1748.
Elsevier DOI 1410
Clustering validation BibRef

Wong, T.T.[Tzu-Tsung],
Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation,
PR(48), No. 9, 2015, pp. 2839-2846.
Elsevier DOI 1506
Classification BibRef

Henriques, R.[Rui], Antunes, C.[Cláudia], Madeira, S.C.[Sara C.],
A structured view on pattern mining-based biclustering,
PR(48), No. 12, 2015, pp. 3941-3958.
Elsevier DOI 1509
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IEEE DOI 1907
Calibration, Computational modeling, Support vector machines, Estimation, Symmetric matrices, Training data, polynomial regression BibRef

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Pattern classification, Class imbalance, Performance metrics, F-measure, Visualization tools, Video face recognition BibRef

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Error probability, Testing, Covariance matrices, Gaussian noise, Random variables, Noise measurement, Matrices, Error probability, multivariate Gaussian noise BibRef

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Traditional machine learning, Deep learning, Support vector machines, Convolutional neural networks BibRef

Tan, Q.L.[Qu-Lin], Guo, B.[Bin], Hu, J.[Jun], Dong, X.F.[Xiao-Feng], Hu, J.P.[Ji-Ping],
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Multi-classifier combination, Deep learning algorithm, Object-oriented, Remote sensing image, Information extraction BibRef

Espinosa, S.[Sebastian], Silva, J.F.[Jorge F.], Piantanida, P.[Pablo],
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IEEE DOI 2102
Binary Hypothesis Testing (BHT). Convergence, Error probability, Cascading style sheets, Upper bound, Task analysis, Bayes methods, Tools, Error exponent, concentration inequalities BibRef

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Cross validation, CV, Uncertainty, Variance, Influence function, Influence curve, Components of variance, Classification BibRef

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Clustering, Clustering validity index, Internal index, Density-based cluster validation, Unsupervised BibRef

Lee, J.[Jaemin], Han, M.[Minseok], Lee, J.S.[Jong-Seok],
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Clustering, Center evolution, Convergence analysis, Ergodic Markov chain, Faster algorithm BibRef

Tank, A.[Alex], Covert, I.[Ian], Foti, N.[Nicholas], Shojaie, A.[Ali], Fox, E.B.[Emily B.],
Neural Granger Causality,
PAMI(44), No. 8, August 2022, pp. 4267-4279.
IEEE DOI 2207
Error analysis of reduced model and full model. Time series analysis, Neural networks, Reactive power, Recurrent neural networks, Predictive models, Estimation, interpretability BibRef

Li, C.Y.[Cong-Yu], Li, Z.[Zhen], Liu, X.X.[Xin-Xin], Li, S.T.[Shu-Tao],
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Foody, G.M.[Giles M.],
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Ali, M.[Mehdi], Berrendorf, M.[Max], Hoyt, C.T.[Charles Tapley], Vermue, L.[Laurent], Galkin, M.[Mikhail], Sharifzadeh, S.[Sahand], Fischer, A.[Asja], Tresp, V.[Volker], Lehmann, J.[Jens],
Bringing Light Into the Dark: A Large-Scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework,
PAMI(44), No. 12, December 2022, pp. 8825-8845.
IEEE DOI 2212
All code and tests:
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Maldonado, S.[Sebastián], Saltos, R.[Ramiro], Vairetti, C.[Carla], Delpiano, J.[José],
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Elsevier DOI 2212
Induced ordered weighted average, Kernel k-means, OWA operators, Dataset shift, Clustering BibRef

Yamaguchi, T.[Takumi], Murakawa, M.[Masahiro],
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PRL(164), 2022, pp. 191-199.
Elsevier DOI 2212
Reliability of inference results. Selective classification, Classification with a reject option, Metric learning, Mixup, Uncertainty estimation, Confidence calibration BibRef

Ahmadzadeh, A.[Azim], Kempton, D.J.[Dustin J.], Martens, P.C.[Petrus C.], Angryk, R.A.[Rafal A.],
Contingency Space: A Semimetric Space for Classification Evaluation,
PAMI(45), No. 2, February 2023, pp. 1501-1513.
IEEE DOI 2301
Measurement, Extraterrestrial measurements, Performance evaluation, Analytical models, Indexes, Sensitivity, knowledge representation formalisms and methods BibRef

Wang, J.T.[Jie-Ting], Qian, Y.H.[Yu-Hua], Li, F.J.[Fei-Jiang], Liang, J.[Jiye], Zhang, Q.F.[Qing-Fu],
Generalization Performance of Pure Accuracy and its Application in Selective Ensemble Learning,
PAMI(45), No. 2, February 2023, pp. 1798-1816.
IEEE DOI 2301
Loss measurement, Particle measurements, Atmospheric measurements, Support vector machines, selective ensemble learning BibRef

Fränti, P.[Pasi], Mariescu-Istodor, R.[Radu],
Soft precision and recall,
PRL(167), 2023, pp. 115-121.
Elsevier DOI 2303
Soft measures, Evaluation, Precision, Recall, F-score BibRef

Masana, M.[Marc], Liu, X.[Xialei], Twardowski, B.[Bartlomiej], Menta, M.[Mikel], Bagdanov, A.D.[Andrew D.], van de Weijer, J.[Joost],
Class-Incremental Learning: Survey and Performance Evaluation on Image Classification,
PAMI(45), No. 5, May 2023, pp. 5513-5533.
IEEE DOI 2304
Task analysis, Training, Network architecture, Learning systems, Image classification, Training data, Privacy, catastrophic forgetting BibRef

Viering, T.[Tom], Loog, M.[Marco],
The Shape of Learning Curves: A Review,
PAMI(45), No. 6, June 2023, pp. 7799-7819.
IEEE DOI 2305
Training, Shape, Behavioral sciences, Training data, Loss measurement, Computational modeling, Standards, regression BibRef

Chen, Q.Q.[Qing-Qiang], Cao, F.Y.[Fu-Yuan], Xing, Y.[Ying], Liang, J.[Jiye],
Evaluating Classification Model Against Bayes Error Rate,
PAMI(45), No. 8, August 2023, pp. 9639-9653.
IEEE DOI 2307
Task analysis, Noise measurement, Estimation, Error analysis, Reliability theory, Data models, Support vector machines, percolation theory BibRef

Zhou, J.K.[Jing-Kai], Wang, P.C.[Pi-Chao], Tang, J.S.[Jia-Sheng], Wang, F.[Fan], Liu, Q.[Qiong], Li, H.[Hao], Jin, R.[Rong],
What Limits the Performance of Local Self-attention?,
IJCV(131), No. 10, October 2023, pp. 2516-2528.
Springer DOI 2309
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Qu, H.X.[Hao-Xuan], Foo, L.G.[Lin Geng], Li, Y.C.[Yan-Chao], Liu, J.[Jun],
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Campello, B.S.C.[Betania Silva Carneiro], Duarte, L.T.[Leonardo Tomazeli], Romano, J.M.T.[João Marcos Travassos],
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Elsevier DOI 2310
Adaptive prediction methods, Multi-criteria decision analysis, MCDA, Temporal analysis, Multi-period, Dynamic multi-attribute decision making BibRef

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Huang, Y.[Yan], Zhang, Z.[Zhang], Huang, Y.[Yan], Wu, Q.[Qiang], Huang, H.[Han], Zhong, Y.[Yi], Wang, L.[Liang],
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A coincidence detection perspective for the maximum mean discrepancy,
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Aids in interpretability. Coincidence detection, Maximum mean discrepancy, Collision entropy, Hypothesis test, Grassberger-Procaccia method BibRef

Liu, Z.W.[Zi-Wei], Miao, Z.Q.[Zhong-Qi], Zhan, X.H.[Xiao-Hang], Wang, J.Y.[Jia-Yun], Gong, B.Q.[Bo-Qing], Yu, S.X.[Stella X.],
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IEEE DOI 2402
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CVPR19(2532-2541).
IEEE DOI 2002
Tail, Visualization, Head, Training, Task analysis, Measurement, Long-tailed recognition, few-shot learning, active learning BibRef


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Identification of Systematic Errors of Image Classifiers on Rare Subgroups,
ICCV23(5041-5050)
IEEE DOI 2401
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Minatel, D.[Diego], Parmezan, A.R.S.[Antonio R. S.], Cúri, M.[Mariana], de Andrade Lopes, A.[Alneu],
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Mukhoti, J.[Jishnu], Kirsch, A.[Andreas], van Amersfoort, J.[Joost], Torr, P.H.S.[Philip H.S.], Gal, Y.[Yarin],
Deep Deterministic Uncertainty: A New Simple Baseline,
CVPR23(24384-24394)
IEEE DOI 2309
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Jiang, Q.[Qian], Chen, C.Y.[Chang-You], Zhao, H.[Han], Chen, L.Q.[Li-Qun], Ping, Q.[Qing], Tran, S.D.[Son Dinh], Xu, Y.[Yi], Zeng, B.[Belinda], Chilimbi, T.[Trishul],
Understanding and Constructing Latent Modality Structures in Multi-Modal Representation Learning,
CVPR23(7661-7671)
IEEE DOI 2309
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Wang, D.B.[Deng-Bao], Li, L.Q.[Lan-Qing], Zhao, P.[Peilin], Heng, P.A.[Pheng-Ann], Zhang, M.L.[Min-Ling],
On the Pitfall of Mixup for Uncertainty Calibration,
CVPR23(7609-7618)
IEEE DOI 2309
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Agarwal, A.[Akshay], Ratha, N.[Nalini], Vatsa, M.[Mayank], Singh, R.[Richa],
Benchmarking Robustness Beyond LP Norm Adversaries,
AdvRob22(342-359).
Springer DOI 2304
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Risser-Maroix, O.[Olivier], Chamand, B.[Benjamin],
What can we Learn by Predicting Accuracy?,
WACV23(2389-2398)
IEEE DOI 2302
Correlation, Pipelines, Machine learning, Feature extraction, Linear programming, Task analysis, ethical computer vision BibRef

Ueda, R.[Ryosuke], Takeuchi, K.[Koh], Kashima, H.[Hisashi],
Mitigating Observatio> ICPR22,
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IEEE DOI 2212
Crowdsourcing, Costs, Correlation, Supervised learning, Robustness, Complexity theory BibRef

Pliushch, I.[Iuliia], Mundt, M.[Martin], Lupp, N.[Nicolas], Ramesh, V.[Visvanathan],
When Deep Classifiers Agree: Analyzing Correlations Between Learning Order and Image Statistics,
ECCV22(VIII:397-413).
Springer DOI 2211
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Zhu, F.[Fei], Cheng, Z.[Zhen], Zhang, X.Y.[Xu-Yao], Liu, C.L.[Cheng-Lin],
Rethinking Confidence Calibration for Failure Prediction,
ECCV22(XXV:518-536).
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WWW Link. Confidence. BibRef

Moayeri, M.[Mazda], Pope, P.[Phillip], Balaji, Y.[Yogesh], Feizi, S.[Soheil],
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes,
CVPR22(19065-19075)
IEEE DOI 2210
Training, Location awareness, Adaptation models, Visualization, Sensitivity, Annotations, Datasets and evaluation, Visual reasoning BibRef

Poms, F.[Fait], Sarukkai, V.[Vishnu], Mullapudi, R.T.[Ravi Teja], Sohoni, N.S.[Nimit S.], Mark, W.R.[William R.], Ramanan, D.[Deva], Fatahalian, K.[Kayvon],
Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories,
ICCV21(10685-10694)
IEEE DOI 2203
Monte Carlo methods, Machine learning algorithms, Costs, Computational modeling, Training data, Machine learning, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Moraes, D., Benevides, P., Moreira, F.D., Costa, H., Caetano, M.,
Exploring the Use of Classification Uncertainty to Improve Classification Accuracy,
ISPRS21(B3-2021: 81-86).
DOI Link 2201
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Wang, C.Y.[Chien-Yao], Liao, H.Y.M.[Hong-Yuan Mark], Yeh, I.H.[I-Hau], Chuang, Y.Y.[Yung-Yu], Lin, Y.L.[Youn-Long],
Exploring the power of lightweight YOLOv4,
LPCV21(779-788)
IEEE DOI 2112
Training, Learning systems, Power demand, Computational modeling, Neural networks, Pipelines, Object detection BibRef

Deng, W.J.[Wei-Jian], Zheng, L.[Liang],
AutoEval: Are Labels Always Necessary for Classifier Accuracy Evaluation?,
PAMI(46), No. 3, March 2024, pp. 1868-1880.
IEEE DOI 2402
BibRef
Earlier:
Are Labels Always Necessary for Classifier Accuracy Evaluation?,
CVPR21(15064-15073)
IEEE DOI 2111
Training, Correlation, Task analysis, Predictive models, Standards, Neural networks, Image color analysis, dataset-level regression. Computational modeling, Rendering (computer graphics), Object recognition BibRef

Sensoy, M.[Murat], Saleki, M.[Maryam], Julier, S.[Simon], Aydogan, R.[Reyhan], Reid, J.[John],
Misclassification Risk and Uncertainty Quantification in Deep Classifiers,
WACV21(2483-2491)
IEEE DOI 2106
Training, Deep learning, Uncertainty, Decision making, Predictive models BibRef

Shen, Y.C.[Yi-Chen], Zhang, Z.L.[Zhi-Lu], Sabuncu, M.R.[Mert R.], Sun, L.[Lin],
Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation,
WACV21(707-716)
IEEE DOI 2106
Deep learning, Training, Uncertainty, Computational modeling, Semantics, Estimation BibRef

Serrat, J.[Joan], Ruiz, I.[Idoia],
Rank-based ordinal classification,
ICPR21(8069-8076)
IEEE DOI 2105
Some errors are worse than others. Measurement, Satellites, Buildings, Estimation, Network architecture, Benchmark testing BibRef

Burghouts, G.J.[Gertjan J.],
Task-specific Novel Object Characterization,
HCAU20(447-455).
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Deal with unknown objects. BibRef

Gao, J.[Jian], Hua, Y.[Yang], Hu, G.S.[Guo-Sheng], Wang, C.[Chi], Robertson, N.M.[Neil M.],
Reducing Distributional Uncertainty by Mutual Information Maximisation and Transferable Feature Learning,
ECCV20(XXIII:587-605).
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Philion, J.[Jonah], Kar, A.[Amlan], Fidler, S.[Sanja],
Learning to Evaluate Perception Models Using Planner-Centric Metrics,
CVPR20(14052-14061)
IEEE DOI 2008
Code, Evaluation.
WWW Link. Detectors, Task analysis, Object detection, Noise measurement, Trajectory BibRef

Wang, P.[Pei], Vasconcelos, N.M.[Nuno M.],
SCOUT: Self-Aware Discriminant Counterfactual Explanations,
CVPR20(8978-8987)
IEEE DOI 2008
Visualization, Heat Maps, Task analysis, Training, Protocols, BibRef

Simon, D., Farber, M., Goldenberg, R.,
Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles,
VL3W20(3984-3988)
IEEE DOI 2008
Training, Data models, Semantics, Manuals, Predictive models, Task analysis, Labeling BibRef

Branchaud-Charron, F.[Frederic], Achkar, A.[Andrew], Jodoin, P.M.[Pierre-Marc],
Spectral Metric for Dataset Complexity Assessment,
CVPR19(3210-3219).
IEEE DOI 2002
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Das, D.[Dipanjan], Ghosh, R.[Ratul], Bhowmick, B.[Brojeshwar],
Deep Representation Learning Characterized by Inter-Class Separation for Image Clustering,
WACV19(628-637)
IEEE DOI 1904
feature extraction, image representation, learning (artificial intelligence), neural nets, Deep learning BibRef

Chen, Z.Q.[Zhi-Qiang], Du, C.D.[Chang-De], Huang, L.J.[Li-Jie], Li, D.[Dan], He, H.G.[Hui-Guang],
Improving Image Classification Performance with Automatically Hierarchical Label Clustering,
ICPR18(1863-1868)
IEEE DOI 1812
Task analysis, Training, Merging, Dogs, Labeling, Machine learning, Clustering algorithms BibRef

Fawzi, A.[Alhussein], Frossard, P.[Pascal],
Measuring the effect of nuisance variables on classifiers,
BMVC16(xx-yy).
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Mendiola-Lau, V.[Victor], Silva Mata, F.J.[Francisco José], Calaña, Y.P.[Yenisel Plasencia], Bustamante, I.T.[Isneri Talavera], de Marsico, M.[Maria],
Bio-Chemical Data Classification by Dissimilarity Representation and Template Selection,
CIARP17(374-381).
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Barddal, J.P., Gomes, H.M., de Souza Britto, A., Enembreck, F.,
A benchmark of classifiers on feature drifting data streams,
ICPR16(2180-2185)
IEEE DOI 1705
Adaptation models, Benchmark testing, Context, Decision trees, Detectors, Feature extraction, Generators BibRef

Wang, D.[Dong], Tan, X.Y.[Xiao-Yang],
Label-Denoising Auto-encoder for Classification with Inaccurate Supervision Information,
ICPR14(3648-3653)
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Cabrera, G.F.[Guillermo F.], Miller, C.J.[Christopher J.], Schneider, J.[Jeff],
Systematic Labeling Bias: De-biasing Where Everyone is Wrong,
ICPR14(4417-4422)
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Hajizadeh, S.[Siamak], Li, Z.L.[Zi-Li], Dollevoet, R.P.B.J.[Rolf P.B.J.], Tax, D.M.J.[David M.J.],
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Santos, J.M.[Jorge M.], Embrechts, M.[Mark],
A Family of Two-Dimensional Benchmark Data Sets and Its Application to Comparing Different Cluster Validation Indices,
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Aghazadeh, O.[Omid], Carlsson, S.[Stefan],
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Loyola-González, O.[Octavio], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[Jesús Ariel], García-Borroto, M.[Milton],
A Novel Contrast Pattern Selection Method for Class Imbalance Problems,
MCPR17(42-52).
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Correlation of Resampling Methods for Contrast Pattern Based Classifiers,
MCPR15(93-102).
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García-Borroto, M.[Milton], Loyola-Gonzalez, O.[Octavio], Martínez-Trinidad, J.F.[José Francisco],
Comparing Quality Measures for Contrast Pattern Classifiers,
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Welinder, P.[Peter], Welling, M.[Max], Perona, P.[Pietro],
A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration,
CVPR13(3262-3269)
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Wei, S.M.[Si-Ming], Yu, Y.Z.[Yi-Zhou],
Subspace segmentation with a Minimal Squared Frobenius Norm Representation,
ICPR12(3509-3512).
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Berrar, D.[Daniel],
Null QQ plots: A simple graphical alternative to significance testing for the comparison of classifiers,
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Li, J.[James], Sonmez, A.[Abdullah], Cataltepe, Z.[Zehra], Bax, E.[Eric],
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Error Estimation, Classification Accuracy .


Last update:Mar 16, 2024 at 20:36:19