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.
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

Boursinos, D.[Dimitrios], Koutsoukos, X.[Xenofon],
Efficient probability intervals for classification using inductive venn predictors,
PR(143), 2023, pp. 109734.
Elsevier DOI 2310
Deep neural networks, Assurance monitoring, Inductive Venn predictors, Probability intervals BibRef

Wiroonsri, N.[Nathakhun],
Clustering performance analysis using a new correlation-based cluster validity index,
PR(145), 2024, pp. 109910.
Elsevier DOI 2311
Clustering algorithm, Cluster validity measure, Data partitions, Correlation, Pattern recognition, Marketing BibRef

Afser, H.[Hüseyin], Györfi, L.[László], Walk, H.[Harro],
Classification With Repeated Observations,
SPLetters(30), 2023, pp. 1522-1526.
IEEE DOI 2311
BibRef

Tang, G.Y.[Guang-Yi], Ni, J.J.[Jian-Jun], Zhao, Y.H.[Yong-Hao], Gu, Y.[Yang], Cao, W.D.[Wei-Dong],
A Survey of Object Detection for UAVs Based on Deep Learning,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef

Rao, S.[Sukrut], Böhle, M.[Moritz], Schiele, B.[Bernt],
Better Understanding Differences in Attribution Methods via Systematic Evaluations,
PAMI(46), No. 6, June 2024, pp. 4090-4101.
IEEE DOI 2405
Measurement, Location awareness, Head, Visualization, Systematics, Informatics, Attribution evaluation, attribution methods, model faithfulness BibRef


Roschewitz, M.[Mélanie], Glocker, B.[Ben],
Distance Matters For Improving Performance Estimation Under Covariate Shift,
Uncertainty23(4551-4561)
IEEE DOI Code:
WWW Link. 2401
How good is the network really. BibRef

Wagner, B.[Baptiste], Pellerin, D.[Denis], Huet, S.[Sylvain],
Comparative Study of Natural Replay and Experience Replay in Online Object Detection,
VCL23(3587-3595)
IEEE DOI 2401
Learning biased to Common objects. BibRef

Chiu, M.C.[Ming-Chang], Chen, P.Y.[Pin-Yu], Ma, X.Z.[Xue-Zhe],
Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image Classification,
ICCV23(4933-4943)
IEEE DOI 2401
BibRef

Tiwari, R.[Rishabh], Chavan, A.[Arnav], Gupta, D.[Deepak], Mago, G.[Gowreesh], Gupta, A.[Animesh], Gupta, A.[Akash], Sharan, S.[Suraj], Yang, Y.K.[Yu-Kun], Zhao, S.W.[Shan-Wei], Wang, S.H.[Shi-Hao], Kwak, Y.J.[Young-Jun], Jeong, S.[Seonghun], Lee, Y.[Yunseung], Kim, C.[Changick], Kim, S.B.[Su-Bin], Gankhuyag, G.[Ganzorig], Jung, H.[Ho], Ryu, J.[Junwhan], Kim, H.[HaeMoon], Kim, B.H.[Byeong H.], Vo, T.[Tu], Zaheer, S.[Sheir], Holston, A.[Alexander], Park, C.[Chan], Dixit, D.[Dheemant], Lele, N.[Nahush], Bhushan, K.[Kushagra], Bhowmick, D.[Debjani], Arya, D.[Devanshu], Gulshad, S.[Sadaf], Habibian, A.[Amirhossein], Ghodrati, A.[Amir], Bejnordi, B.[Babak], Gupta, J.[Jai], Liu, Z.[Zhuang], Yu, J.[Jiahui], Prasad, D.[Dilip], Shen, Z.Q.[Zhi-Qiang],
RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning,
REDLCV23(1526-1535)
IEEE DOI 2401
BibRef

Bohdal, O.[Ondrej], Tian, Y.B.[Yin-Bing], Zong, Y.S.[Yong-Shuo], Chavhan, R.[Ruchika], Li, D.[Da], Gouk, H.[Henry], Guo, L.[Li], Hospedales, T.M.[Timothy M.],
Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn,
CVPR23(7693-7703)
IEEE DOI 2309
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 .


Last update:Jul 13, 2024 at 15:27:21