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.
Elsevier DOI
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
Chen, J.[Joya],
Liu, D.[Dong],
Xu, T.[Tong],
Wu, S.W.[Shi-Wei],
Cheng, Y.F.[Yi-Fei],
Chen, E.[Enhong],
Is Heuristic Sampling Necessary in Training Deep Object Detectors?,
IP(30), 2021, pp. 8454-8467.
IEEE DOI
2110
Training, Detectors, Sampling methods, Task analysis,
Object detection, Proposals, Pipelines, Object detection, sampling-free
BibRef
Seghier, M.L.[Mohamed L.],
Ten simple rules for reporting machine learning methods
implementation and evaluation on biomedical data,
IJIST(32), No. 1, 2022, pp. 5-11.
DOI Link
2201
biomedical data processing, classification and segmentation,
machine learning methods, performance evaluation, performance metrics
BibRef
Liu, D.X.[Dong-Xu],
Zhao, H.Q.[Hai-Quan],
Statistics Behavior of Individual-Weighting-Factors SSAF Algorithm
Under Errors-in-Variables Model,
SPLetters(30), 2023, pp. 319-323.
IEEE DOI
2304
Signal processing algorithms, Behavioral sciences,
Performance analysis, Steady-state, Background noise, statistics analysis
BibRef
Haghpanah, M.A.[Mohammad Amin],
Tale-Masouleh, M.[Mehdi],
Kalhor, A.[Ahmad],
Determining the trustworthiness of DNNs in classification tasks using
generalized feature-based confidence metric,
PR(142), 2023, pp. 109683.
Elsevier DOI
2307
Machine learning, Deep learning, Confidence metric,
Generalized feature-based confidence, Model trust score,
Feature quality evaluation
BibRef
Ji, Y.[Yilin],
Kaestner, D.[Daniel],
Wirth, O.[Oliver],
Wressnegger, C.[Christian],
Randomness is the Root of All Evil: More Reliable Evaluation of Deep
Active Learning,
WACV23(3932-3941)
IEEE DOI
2302
Deep learning, Neural networks, Stability analysis,
Reproducibility of results, Hardware, Appraisal,
and algorithms (including transfer)
BibRef
Giloni, A.[Amit],
Grolman, E.[Edita],
Elovici, Y.[Yuval],
Shabtai, A.[Asaf],
FEPC: Fairness Estimation Using Prototypes and Critics for Tabular
Data,
ICPR22(4877-4884)
IEEE DOI
2212
Maximum likelihood estimation, Prototypes, Machine learning,
Benchmark testing, Cognition, Data models
BibRef
Cho, Y.[Yooshin],
Kim, Y.S.[Young-Soo],
Cho, H.[Hanbyel],
Ahn, J.[Jaesung],
Hong, H.G.[Hyeong Gwon],
Kim, J.[Junmo],
Rethinking Efficacy of Softmax for Lightweight Non-local Neural
Networks,
ICIP22(1031-1035)
IEEE DOI
2211
Visualization, Costs, Computational modeling, Neural networks,
Robustness, Computational efficiency, Attention, Non-local block, Transformer
BibRef
Zheng, J.Q.[Jian-Qiao],
Ramasinghe, S.[Sameera],
Li, X.Q.[Xue-Qian],
Lucey, S.[Simon],
Trading Positional Complexity vs Deepness in Coordinate Networks,
ECCV22(XXVII:144-160).
Springer DOI
2211
BibRef
Khanal, B.[Bidur],
Kanan, C.[Christopher],
How Does Heterogeneous Label Noise Impact Generalization in Neural
Nets?,
ISVC21(II:229-241).
Springer DOI
2112
BibRef
Lyu, Z.Y.[Zhao-Yang],
Guo, M.H.[Ming-Hao],
Wu, T.[Tong],
Xu, G.D.[Guo-Dong],
Zhang, K.[Kehuan],
Lin, D.[Dahua],
Towards Evaluating and Training Verifiably Robust Neural Networks,
CVPR21(4306-4315)
IEEE DOI
2111
Training, Codes, Scalability, Neurons, Robustness, Pattern recognition
BibRef
Korkmaz, E.[Ezgi],
Inaccuracy of State-Action Value Function For Non-Optimal Actions in
Adversarially Trained Deep Neural Policies,
RCV21(2323-2327)
IEEE DOI
2109
Training, Resistance, Deep learning, Systematics, Perturbation methods.
BibRef
Xuan, H.[Hong],
Stylianou, A.[Abby],
Liu, X.T.[Xiao-Tong],
Pless, R.[Robert],
Hard Negative Examples are Hard, but Useful,
ECCV20(XIV:126-142).
Springer DOI
2011
BibRef
Bhaskaruni, D.[Dheeraj],
Moss, F.P.[Fiona Patricia],
Lan, C.[Chao],
Estimating Prediction Qualities without Ground Truth:
A Revisit of the Reverse Testing Framework,
ICPR18(49-54)
IEEE DOI
1812
Testing, Training, Predictive models, Measurement, Anomaly detection,
Task analysis
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 .