14.5.3 Evaluation and Analysis of Learning Techniques

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Evaluation, Learning. Learning.

Shvaytser, H.,
Learnable and Nonlearnable Visual Concepts,
PAMI(12), No. 5, May 1990, pp. 459-466.
IEEE DOI BibRef 9005
Earlier: ICCV88(264-268).
IEEE DOI BibRef

Bradley, A.P.[Andrew P.],
The Use of the Area Under the ROC Curve in the Evaluation of Machine Learning Algorithms,
PR(30), No. 7, July 1997, pp. 1145-1159.
WWW Link. 9707
BibRef

Bradley, A.P.[Andrew P.],
ROC curve equivalence using the Kolmogorov-Smirnov test,
PRL(34), No. 5, 1 April 2013, pp. 470-475.
Elsevier DOI 1303
ROC curves; KS-test; AUC; Specificity; Sensitivity; Coherence BibRef

Bradley, A.P., Longstaff, I.D.,
Sample size estimation using the receiver operating characteristic curve,
ICPR04(IV: 428-431).
IEEE DOI 0409
BibRef

Gu, H.Z.[Han-Zhong], Takahashi, H.[Haruhisa],
How Bad May Learning Curves Be?,
PAMI(22), No. 10, October 2000, pp. 1155-1167.
IEEE DOI 0011
BibRef

Gifford, H.C., King, M.A., Pretorius, P.H., Wells, R.G.,
A Comparison of Human and Model Observers in Multislice LROC Studies,
MedImg(24), No. 2, February 2005, pp. 160-169.
IEEE Abstract. 0501
BibRef

Wang, F., Dobre, O.A., Chan, C., Zhang, J.,
Fold-based Kolmogorov-Smirnov Modulation Classifier,
SPLetters(23), No. 7, July 2016, pp. 1003-1007.
IEEE DOI 1608
modulation BibRef


Ramos-Pollán, R.[Raúl], Guevara-López, M.Á.[Miguel Ángel], Oliveira, E.[Eugénio],
Introducing ROC Curves as Error Measure Functions: A New Approach to Train ANN-Based Biomedical Data Classifiers,
CIARP10(517-524).
Springer DOI 1011
BibRef

Pungprasertying, P.[Prasertsak], Chatpatanasiri, R.[Ratthachat], Kijsirikul, B.[Boonserm],
Migration Analysis: An Alternative Approach for Analyzing Learning Performance,
ICPR06(II: 837-840).
IEEE DOI 0609
BibRef

Shimizu, S., Ohyama, W., Wakabayashi, T., Kimura, F.,
Mirror image learning for autoassociative neural networks,
ICDAR03(804-808).
IEEE DOI 0311
BibRef

Shi, M.[Meng], Wakabayashi, T., Ohyama, W., Kimura, F.,
Comparative study on mirror image learning (MIL) and GLVQ,
ICPR02(II: 248-252).
IEEE DOI 0211
BibRef
Earlier: A2, A1, A3, A4:
A comparative study on mirror image learning and ALSM,
FHR02(151-156).
IEEE Top Reference. 0209
BibRef

Burege, M.J., Burger, W.,
Learning Visual Ideals,
CIAP97(II: 316-323).
Springer DOI 9709
Compares 24 different approaches for learning applied to object recognition. BibRef

Blackburn, M.R.[Michael R.], and Nguyen, H.G.[Hoa G.],
Learning in Robot Vision Directed Reaching: A Comparison of Methods,
ARPA94(I:781-788). BibRef 9400

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Learning Object Descriptions, Object Recognition .


Last update:Sep 18, 2017 at 11:34:11