14.1.5 Classifier, Performance Evaluation, Errors, Comparisons

Chapter Contents (Back)
Evaluation, Classifiers. Comparisons.

Mattson, R.L., and Firschein, O.,
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Viscolani, B.[Bruno],
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A sequential organization of the computations arising from pattern recognizers by absolute comparison is suggested in order to reduce the mean computational time involved. BibRef

Hanley, J., McNeil, B.,
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Viscolani, B.[Bruno],
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Goin, J.E.[James E.], Fritz, S.L.[Steven L.],
A Matrix Approach to Data Base Exploration: Analysis of Classifier Results,
PR(16), No. 2, 1983, pp. 243-252.
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Raveh, A.[Adi],
Preference structure analysis: A nonmetric approach,
PR(16), No. 2, 1983, pp. 253-259.
WWW Version. 0309
Suggest a nonmetric procedure in which goodness of discrimination is higher than or equal to that of Fisher's discriminant function. very different discriminant functions could yield the very same number of errors. BibRef

Fowlkes, E.B., and Mallows, C.L.,
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ADAJ(78), No. 383, 1983, pp. 553-569. BibRef 8300

Flick, T.E., and Jones, L.K.,
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Chernick, M.C., Murthy, V.K., and Nealy, C.D.,
Application of Bootstrap and Other Resampling Techniques: Evaluation of Classifier Performance,
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Jain, A.K., Dubes, R.C., and Chen, C.C.,
Bootstrap Techniques for Error Estimation,
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Colussi, L.,
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Srivastava, A.[Anurag], Murty, M.N.,
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Tang, Y.Y., Qu, Y.Z., Suen, C.Y.,
Multiple-level information source and entropy-reduced transformation models,
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Analyse systematically the changes in entropy which occur in the different stages of a pattern recognizer. BibRef

Gluhchev, G., Shalev, S.,
The Systematic-Error Detection as a Classification Problem,
PRL(17), No. 12, October 25 1996, pp. 1233-1238. 9612
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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.,
Evaluation of Pattern Classifiers: Testing the Significance of Classification Efficiency Using an Exact Probability Technique,
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Nyssen, E.,
Evaluation of Pattern Classifiers: Applying a Monte Carlo Significance Test to the Classification Efficiency,
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Barbosa, P.M., Casterad, M.A., Herrero, J.,
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SanMiguel-Ayanz, J., Biging, G.S.,
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Denceux, T.,
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Bokka, V., Olariu, S., Schwing, J.L., Wilson, L., Zomaya, A.,
A Time-Optimal Solution to a Classification Problem in Ordered Functional Domains, with Applications,
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Zhou, J.Y., Lopresti, D.P.,
Improving Classifier Performance Through Repeated Sampling,
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Flygare, A.M.,
A Comparison of Contextual Classification Methods Using Landsat TM,
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Lerner, B., Guterman, H., Aladjem, M., Dinstein, I., Romem, Y.,
On Pattern Classification with Sammons Nonlinear Mapping: An Experimental-Study,
PR(31), No. 4, April 1998, pp. 371-381.
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See also nonlinear mapping for data structure analysis, A. BibRef

Aladjem, M.[Mayer], Dinstein, I.[Its'hak],
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ICPR92(II:101-104).
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Stehman, S.V., Czaplewski, R.L.,
Design and Analysis for Thematic Map Accuracy Assessment: Fundamental Principles,
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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.,
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Sohn, S.Y.[So Young],
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PAMI(21), No. 11, November 1999, pp. 1137-1144.
IEEE Abstract.
WWW Version. 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.
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Data analysis to partition complex regression surface into simpler surfaces (features). BibRef

Andersson, A.[Arne], Davidsson, P.[Paul], Lindén, J.[Johan],
Measure-based classifier performance evaluation,
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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 Abstract.
WWW Version. 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 Abstract.
WWW Version. 0206
BibRef
Earlier:
Improving the Classification Accuracy of the Scanning N-tuple Method,
ICPR00(Vol II: 1046-1049).
IEEE DOI Link
HTML Version. 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.
WWW Version. 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.
HTML Version. 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.],
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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.
WWW Version. 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 See also Optimization by Simulated Annealing. BibRef

Sandri, L.[Laura], Marzocchi, W.[Warner],
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PR(37), No. 3, March 2004, pp. 447-461.
WWW Version. 0401
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Toh, K.A.[Kar-Ann], Tran, Q.L.[Quoc-Long], Srinivasan, D.[Dipti],
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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],
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SMC-B(35), No. 5, October 2005, pp. 1079-1091.
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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.
WWW Version. 0407
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Solberg, A.H.S.[Anne H. Schistad],
Flexible nonlinear contextual classification,
PRL(25), No. 13, 1 October 2004, pp. 1501-1508.
WWW Version. 0410
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Earlier:
Nonlinear Contextual Classification: A Comparative Study,
SCIA01(O-Th3A). 0206
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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.
WWW Version. 0501
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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. 7, July 2005, pp. 1131-1134.
WWW Version. 0505
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Stein, A., Aryal, J., Gort, G.,
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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.
WWW Version. 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
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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.
WWW Version. 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 Link 0609
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Earlier:
Comparison of non-parametric methods for assessing classifier performance in terms of ROC parameters,
AIPR04(190-195).
IEEE DOI Link 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.
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Baraldi, A., Bruzzone, L., Blonda, P.,
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IP(15), No. 8, August 2006, pp. 2208-2225.
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Fawcett, T.[Tom],
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PRL(27), No. 8, June 2006, pp. 861-874.
WWW Version. Classifier evaluation; Evaluation metrics 0605
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Stathakis, D., Vasilakos, A.,
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GeoRS(44), No. 8, August 2006, pp. 2305-2318.
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Stathakis, D.[Demetris], Kanellopoulos, I.[Ioannis],
Global Elevation Ancillary Data for Land-use Classification Using Granular Neural Networks,
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Initial guidelines for the construction of granular neural networks in the remote sensing context. BibRef

Stathakis, D.[Demetris], Kanellopoulos, I.[Ioannis],
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A method for optimal Multi Layer Perceptron topology determination carried out by a genetic algorithm. BibRef

Arbel, R.[Reuven], Rokach, L.[Lior],
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Evaluation measures; Hit-rate; Recall; Receiver operating characteristic BibRef

Nangendo, G.[Grace], Skidmore, A.K.[Andrew K.], van Oosten, H.[Henk],
Mapping East African tropical forests and woodlands: A comparison of classifiers,
PandRS(61), No. 6, February 2007, pp. 393-404.
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Forest classification; Conventional classifiers; Expert System; Classification accuracy; East Africa BibRef

An, S.[Senjian], Liu, W.Q.[Wan-Quan], Venkatesh, S.[Svetha],
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WWW Version. 0704
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Efficient Cross-validation of the Complete Two Stages in KFD Classifier Formulation,
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Model selection; Cross-validation; Kernel methods BibRef

An, S.[Senjian], Peursum, P.[Patrick], Liu, W.Q.[Wan-Quan], Venkatesh, S.[Svetha],
Efficient algorithms for subwindow search in object detection and localization,
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Pham, D.S.[Duc-Son], Venkatesh, S.[Svetha],
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Pham, D.S.[Duc-Son], Venkatesh, S.[Svetha],
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Břcher, P.K., McCloy, K.R.,
Optimizing Image Resolution to Maximize the Accuracy of Hard Classification,
PhEngRS(73), No. 8, August 2007, pp. 893-904.
WWW Version. 0709
The relationship between classification accuracy and within class variances is investigated showing that within class variances are a function of image resolution. BibRef

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.
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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,
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Volkovich, Z., Barzily, Z., Morozensky, L.,
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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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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.
WWW Version. 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.
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Crisp clustering; Cluster validation; Negentropy BibRef


Huang, H.[Haiqiao], Mok, P.Y.[Pik-Yin], Kwok, Y.L.[Yi-Lin], Au, S.C.[Sau-Chuen],
A Parameter Free Approach for Clustering Analysis,
CAIP09(816-823).
Springer DOI Link 0909
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Woloszynski, T.[Tomasz], Kurzynski, M.[Marek],
On a New Measure of Classifier Competence Applied to the Design of Multiclassifier Systems,
CIAP09(995-1004).
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Zhang, W.[Wei], Deng, H.L.[Hong-Li],
Understanding visual dictionaries via Maximum Mutual Information curves,
ICPR08(1-4).
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Pascual, D.[Damaris], Pla, F.[Filiberto], Salvador Sánchez, J.,
Cluster Stability Assessment Based on Theoretic Information Measures,
CIARP08(219-226).
Springer DOI Link 0809
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Zhang, X.[Xiao], Liang, L.[Lin], Tang, X.[Xiaoou], Shum, H.Y.[Heung-Yeung],
L1 regularized projection pursuit for additive model learning,
CVPR08(1-8).
IEEE DOI Link 0806
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Su, Y.[Yu], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin], Gao, W.[Wen],
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Godoy-Calderón, S.[Salvador], Martínez-Trinidad, J.F., Cortés, M.L.[Manuel Lazo],
Proposal for a Unified Methodology for Evaluating Supervised and Non-supervised Classification Algorithms,
CIARP06(674-685).
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Mouchere, H.[Harold], Anquetil, E.[Eric],
A Unified Strategy to Deal with Different Natures of Reject,
ICPR06(II: 792-795).
WWW Version. 0609
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Pugliese, L.[Luca], Scarpetta, S.[Silvia], Esposito, A.[Anna], Marinaro, M.[Maria],
An Application of Neural and Probabilistic Unsupervised Methods to Environmental Factor Analysis of Multi-spectral Images,
CIAP05(1190-1197).
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compare 2 clustering methods for LANDSAT classification. BibRef

Maillard, P.[Philippe], Clausi, D.A.[David A.],
Comparing Classification Metrics for Labeling Segmented Remote Sensing Images,
CRV05(421-428).
IEEE DOI Link 0505
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Zass, R.[Ron], Shashua, A.[Amnon],
Probabilistic graph and hypergraph matching,
CVPR08(1-8).
IEEE DOI Link 0806
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Zass, R.[Ron], Shashua, A.[Amnon],
A Unifying Approach to Hard and Probabilistic Clustering,
ICCV05(I: 294-301).
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Analysis of clustering approaches. BibRef

Kolsch, T., Keysers, D., Ney, H., Paredes, R.,
Enhancements for local feature based image classification,
ICPR04(I: 248-251).
IEEE DOI Link 0409
Decompose and analyze nearest neighbor search and direct voting for classification. BibRef

Mansilla, E.B., Ho, T.K.[Tin Kam],
On classifier domains of competence,
ICPR04(I: 136-139).
IEEE DOI Link 0409
Where are which classifiers competent. BibRef

Johnson, A.Y.[Amos Y.], Sun, J.[Jie], Bobick, A.F.[Aaron F.],
Predicting Large Population Data Cumulative Match Characteristic Performance from Small Population Data,
AVBPA03(821-829).
HTML Version. 0310
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Lucas, S.M.,
Web-based evaluation and deployment of pattern recognizers,
ICPR02(III: 419-422).
IEEE DOI Link 0211
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Duin, R.P.W., Pekalska, E., Tax, D.M.J.,
The characterization of classification problems by classifier disagreements,
ICPR04(I: 140-143).
IEEE DOI Link 0409
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de Ridder, D., Pekalska, E., Duin, R.P.W.,
The economics of classification: error vs. complexity,
ICPR02(II: 244-247).
IEEE DOI Link 0211
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Varma, M., Zisserman, A.P.,
Classifying materials from images: to cluster or not to cluster?,
Texture02(139-144). 0207
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Yang, M.H., Roth, D., Ahuja, N.,
A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition,
ECCV02(IV: 685 ff.).
HTML Version. 0205
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Huijsmans, N.P., Sebe, N.,
Extended Performance Graphs for Cluster Retrieval,
CVPR01(I:26-31).
IEEE Abstract. 0110
In classification, issue of how to measure the performance expecially when true negatives are dominant. BibRef

Bromiley, P.A., Courtney, P., Thacker, N.A.,
A Case Study in the use of ROC curves for Algorithm Design,
BMVC01(Poster Session 1).
HTML Version. University of Manchester 0110
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Michaels, R., Boult, T.E.,
A Stratified Methodology for Classifier and Recognizer Evaluation,
EEMCV01(xx-yy). 0110
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Tang, M., Xiao, J., Ma, S.D.[Song De],
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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 4, 2010 at 12:17:52