14.1.5 Training Set Size, Sample Size, Analysis, Selection

Chapter Contents (Back)
Training Set. Sample Size.
See also Small Sample Sizes Issues, Data analysis, Training Sets.
See also Unbalanced Datasets, Imbalanced Sample Sizes, Imbalanced Data, Long-Tailed Data.

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Liu, W.J.[Wei-Jian], Liu, J.[Jun], Liu, T.[Tao], Chen, H.[Hui], Wang, Y.L.[Yong-Liang],
Detector Design and Performance Analysis for Target Detection in Subspace Interference,
SPLetters(30), 2023, pp. 618-622.
IEEE DOI 2306
Detectors, Interference, Training data, Covariance matrices, Training, Statistical distributions, Signal detection, subspace interference BibRef

Tsutsui, S.[Satoshi], Fu, Y.W.[Yan-Wei], Crandall, D.[David],
Reinforcing Generated Images via Meta-Learning for One-Shot Fine-Grained Visual Recognition,
PAMI(46), No. 3, March 2024, pp. 1455-1463.
IEEE DOI 2402
Training, Generators, Image recognition, Visualization, Tuning, Training data, Task analysis, Fine-grained visual recognition, meta-learning BibRef


Sudhakar, S.[Sruthi], Hanzelka, J.[Jon], Bobillot, J.[Josh], Randhavane, T.[Tanmay], Joshi, N.[Neel], Vineet, V.[Vibhav],
Exploring the Sim2Real Gap using Digital Twins,
ICCV23(20361-20370)
IEEE DOI 2401
Simulated data. BibRef

Hong, C.[Chunsan], Cha, B.[Byunghee], Kim, B.H.[Bo-Hyung], Oh, T.H.[Tae-Hyun],
Enhancing Classification Accuracy on Limited Data via Unconditional GAN,
LIMIT23(1049-1057)
IEEE DOI 2401
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Matey-Sanz, M.[Miguel], Torres-Sospedra, J.[Joaquín], González-Pérez, A.[Alberto], Casteleyn, S.[Sven], Granell, C.[Carlos],
Analysis and Impact of Training Set Size in Cross-subject Human Activity Recognition,
CIARP23(I:391-405).
Springer DOI 2312
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Feng, W.[Wei], Gao, X.T.[Xin-Ting], Dauphin, G.[Gabriel], Quan, Y.H.[Ying-Hui],
Rotation XGBoost Based Method for Hyperspectral Image Classification with Limited Training Samples,
ICIP23(900-904)
IEEE DOI 2312
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Jeong, J.[Jongheon], Yu, S.[Sihyun], Lee, H.[Hankook], Shin, J.[Jinwoo],
Enhancing Multiple Reliability Measures via Nuisance-Extended Information Bottleneck,
CVPR23(16206-16218)
IEEE DOI 2309

WWW Link. BibRef

Patel, D.[Deep], Sastry, P.S.,
Adaptive Sample Selection for Robust Learning under Label Noise,
WACV23(3921-3931)
IEEE DOI 2302
Training, Knowledge engineering, Deep learning, Heuristic algorithms, Neural networks, Benchmark testing, and algorithms (including transfer) BibRef

Vanderschueren, A.[Antoine], de Vleeschouwer, C.[Christophe],
Are Straight-Through gradients and Soft-Thresholding all you need for Sparse Training?,
WACV23(3797-3806)
IEEE DOI 2302
Training, Neural networks, Estimation, Turning, Computational complexity, Applications: Embedded sensing/real-time techniques BibRef

Deng, S.Q.[Si-Qi], Xiong, Y.J.[Yuan-Jun], Wang, M.[Meng], Xia, W.[Wei], Soatto, S.[Stefano],
Harnessing Unrecognizable Faces for Improving Face Recognition,
WACV23(3413-3422)
IEEE DOI 2302
Image recognition, Quantization (signal), Error analysis, Face recognition, Neural networks, Lighting, Detectors, body pose BibRef

Cheng, M.[Miao], You, X.G.[Xin-Ge],
Leachable Component Clustering,
ICPR22(1858-1864)
IEEE DOI 2212
Dealing with incomplete training data. Data handling, Estimation, Clustering algorithms, Information processing, Mathematical models, Data models, calculation efficiency BibRef

Wad, T.[Tan], Sun, Q.[Qianru], Pranata, S.[Sugiri], Jayashree, K.[Karlekar], Zhang, H.W.[Han-Wang],
Equivariance and Invariance Inductive Bias for Learning from Insufficient Data,
ECCV22(XI:241-258).
Springer DOI 2211
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Wang, K.[Kai], Zhao, B.[Bo], Peng, X.Y.[Xiang-Yu], Zhu, Z.[Zheng], Yang, S.[Shuo], Wang, S.[Shuo], Huang, G.[Guan], Bilen, H.[Hakan], Wang, X.C.[Xin-Chao], You, Y.[Yang],
CAFE: Learning to Condense Dataset by Aligning Features,
CVPR22(12186-12195)
IEEE DOI 2210
Training, Heart, Costs, Performance gain, Pattern recognition, Efficient learning and inferences, Image and video synthesis and generation BibRef

Lokhande, V.S.[Vishnu Suresh], Chakraborty, R.[Rudrasis], Ravi, S.N.[Sathya N.], Singh, V.[Vikas],
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets,
CVPR22(10422-10431)
IEEE DOI 2210
Pool multiple datasets, especially disease data. Representation learning, Neuroimaging, Codes, Atmospheric measurements, Neural networks, Particle measurements, Statistical methods BibRef

Mahmood, R.[Rafid], Lucas, J.[James], Acuna, D.[David], Li, D.Q.[Dai-Qing], Philion, J.[Jonah], Alvarez, J.M.[Jose M.], Yu, Z.D.[Zhi-Ding], Fidler, S.[Sania], Law, M.T.[Marc T.],
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks,
CVPR22(275-284)
IEEE DOI 2210
Costs, Data acquisition, Training data, Estimation, Machine learning, Pattern recognition, Machine learning, Datasets and evaluation, retrieval BibRef

Lemmer, S.J.[Stephan J.], Corso, J.J.[Jason J.],
Ground-Truth or DAER: Selective Re-Query of Secondary Information,
ICCV21(683-694)
IEEE DOI 2203
Training, Degradation, Crowdsourcing, Visualization, Computational modeling, Estimation, Vision + other modalities, Machine learning architectures and formulations BibRef

Kim, T.S.[Tae Soo], Shim, B.[Bohoon], Peven, M.[Michael], Qiu, W.C.[Wei-Chao], Yuille, A.L.[Alan L.], Hager, G.D.[Gregory D.],
Learning from Synthetic Vehicles,
RWSurvil22(500-508)
IEEE DOI 2202

WWW Link. For training vehicle recognition systems. Image recognition, Error analysis, Conferences, Multitasking, Task analysis BibRef

Kataoka, H.[Hirokatsu], Matsumoto, A.[Asato], Yamada, R.[Ryosuke], Satoh, Y.[Yutaka], Yamagata, E.[Eisuke], Inoue, N.[Nakamasa],
Formula-driven Supervised Learning with Recursive Tiling Patterns,
HTCV21(4081-4088)
IEEE DOI 2112
Trained without real data. Training, Visualization, Supervised learning, Image representation, Feature extraction BibRef

Kim, Y.D.[Young-Dong], Yun, J.[Juseung], Shon, H.[Hyounguk], Kim, J.[Junmo],
Joint Negative and Positive Learning for Noisy Labels,
CVPR21(9437-9446)
IEEE DOI 2111
Training, Costs, Filtering, Pipelines, Training data, Pattern recognition BibRef

Jia, R.[Ruoxi], Wu, F.[Fan], Sun, X.[Xuehui], Xu, J.C.[Jia-Cen], Dao, D.[David], Kailkhura, B.[Bhavya], Zhang, C.[Ce], Li, B.[Bo], Song, D.[Dawn],
Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?,
CVPR21(8235-8243)
IEEE DOI 2111
Training, Runtime, Scalability, Data acquisition, Watermarking, Machine learning BibRef

Liu, W.Y.[Wei-Yang], Lin, R.M.[Rong-Mei], Liu, Z.[Zhen], Rehg, J.M.[James M.], Paull, L.[Liam], Xiong, L.[Li], Song, L.[Le], Weller, A.[Adrian],
Orthogonal Over-Parameterized Training,
CVPR21(7247-7256)
IEEE DOI 2111
Training, Scalability, Neurons, Optimized production technology, Pattern recognition BibRef

Cole, E.[Elijah], Aodha, O.M.[Oisin Mac], Lorieul, T.[Titouan], Perona, P.[Pietro], Morris, D.[Dan], Jojic, N.[Nebojsa],
Multi-Label Learning from Single Positive Labels,
CVPR21(933-942)
IEEE DOI 2111
Training, Image resolution, Annotations, Training data, Pattern recognition, Standards BibRef

Hara, K.[Kensho], Ishikawa, Y.[Yuchi], Kataoka, H.[Hirokatsu],
Rethinking Training Data for Mitigating Representation Biases in Action Recognition,
HVU21(3344-3348)
IEEE DOI 2109
Training, Solid modeling, Computational modeling, Dynamics, Training data, Data models BibRef

Bisla, D.[Devansh], Saridena, A.N.[Apoorva Nandini], Choromanska, A.[Anna],
A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs,
TCV21(3264-3274)
IEEE DOI 2109
How error relates to sample size in deep learning. Training, Computational modeling, Measurement uncertainty, Statistical learning, Neural networks, Training data, Safety BibRef

Azuri, I.[Idan], Weinshall, D.[Daphna],
Generative Latent Implicit Conditional Optimization when Learning from Small Sample,
ICPR21(8584-8591)
IEEE DOI 2105
Training, Interpolation, Generators, Optimization, Image classification BibRef

Lokhande, V.S.[Vishnu Suresh], Akash, A.K.[Aditya Kumar], Ravi, S.N.[Sathya N.], Singh, V.[Vikas],
FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret,
ECCV20(XII: 365-381).
Springer DOI 2010
Deal with bias BibRef

Raisi, E.[Elaheh], Bach, S.H.[Stephen H.],
Selecting Auxiliary Data Using Knowledge Graphs for Image Classification with Limited Labels,
VL3W20(4026-4031)
IEEE DOI 2008
Train with not enough data. Task analysis, Training, Visualization, Neural networks, Error analysis, Computational modeling, Semantics BibRef

Wang, Y.[Yang], Cao, Y.[Yang], Zha, Z.J.[Zheng-Jun], Zhang, J.[Jing], Xiong, Z.W.[Zhi-Wei],
Deep Degradation Prior for Low-Quality Image Classification,
CVPR20(11046-11055)
IEEE DOI 2008
The images are low-quality. Degradation, Frequency division multiplexing, Visualization, Feature extraction, Semantics, Training, Task analysis BibRef

Zhang, Z.Z.[Zi-Zhao], Zhang, H.[Han], Arik, S.Ö.[Sercan Ö.], Lee, H.L.[Hong-Lak], Pfister, T.[Tomas],
Distilling Effective Supervision From Severe Label Noise,
CVPR20(9291-9300)
IEEE DOI 2008
Training, Noise measurement, Noise robustness, Labeling, Neural networks, Training data, Data models BibRef

Rao, R., Rao, S., Nouri, E., Dey, D., Celikyilmaz, A., Dolan, B.,
Quality and Relevance Metrics for Selection of Multimodal Pretraining Data,
MULWS20(4109-4116)
IEEE DOI 2008
Task analysis, Measurement, Visualization, Data models, Training, Tagging, Twitter BibRef

Yang, D., Deng, J.,
Learning to Generate 3D Training Data Through Hybrid Gradient,
CVPR20(776-786)
IEEE DOI 2008
Training, Shape, Training data, Optimization, Pipelines, Task analysis BibRef

Mandal, D.[Devraj], Bharadwaj, S.[Shrisha], Biswas, S.[Soma],
A Novel Self-Supervised Re-labeling Approach for Training with Noisy Labels,
WACV20(1370-1379)
IEEE DOI 2006
mCT-S2R (modified co-teaching with self-supervision and relabeling). Training, Task analysis, Noise measurement, Data models, Training data, Computational modeling, Robustness BibRef

Li, Y.[Yi], Vasconcelos, N.M.[Nuno M.],
REPAIR: Removing Representation Bias by Dataset Resampling,
CVPR19(9564-9573).
IEEE DOI 2002
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IEEE DOI 2002
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IEEE DOI 2002
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Actively Seeking and Learning From Live Data,
CVPR19(1940-1949).
IEEE DOI 2002
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Dovrat, O.[Oren], Lang, I.[Itai], Avidan, S.[Shai],
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CVPR19(2755-2764).
IEEE DOI 2002
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Häufel, G., Bulatov, D., Helmholz, P.,
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PIA19(111-118).
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Unceta, I.[Irene], Nin, J.[Jordi], Pujol, O.[Oriol],
Using Copies to Remove Sensitive Data: A Case Study on Fair Superhero Alignment Prediction,
IbPRIA19(I:182-193).
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Unceta, I.[Irene], Nin, J.[Jordi], Pujol, O.[Oriol],
Copying machine learning classifiers,
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Ghosh, P.[Pallabi], Davis, L.S.[Larry S.],
Understanding Center Loss Based Network for Image Retrieval with Few Training Data,
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Alvi, M.[Mohsan], Zisserman, A.[Andrew], Nellåker, C.[Christoffer],
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Improvements to Context Based Self-Supervised Learning,
CVPR18(9339-9348)
IEEE DOI 1812
Image color analysis, Task analysis, Jitter, Standards, Neural networks, Semantics, Network architecture BibRef

Wang, X.D.[Xu-Dong], Liu, Z.W.[Zi-Wei], Yu, S.X.[Stella X.],
Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination,
CVPR21(12581-12590)
IEEE DOI 2111
Training, Codes, Transfer learning, Distributed databases, Benchmark testing, Pattern recognition BibRef

Wu, Z.R.[Zhi-Rong], Xiong, Y.J.[Yuan-Jun], Yu, S.X.[Stella X.], Lin, D.[Dahua],
Unsupervised Feature Learning via Non-parametric Instance Discrimination,
CVPR18(3733-3742)
IEEE DOI 1812
Use neural nets for unsupervised learning. Measurement, Task analysis, Training, Neural networks, Supervised learning, Testing, Visualization BibRef

Novotny, D.[David], Albanie, S.[Samuel], Larlus, D.[Diane], Vedaldi, A.[Andrea],
Self-Supervised Learning of Geometrically Stable Features Through Probabilistic Introspection,
CVPR18(3637-3645)
IEEE DOI 1812
Reduce training data Task analysis, Semantics, Probabilistic logic, Feature extraction, Neural networks, Visualization. BibRef

Keshari, R., Vatsa, M., Singh, R., Noore, A.,
Learning Structure and Strength of CNN Filters for Small Sample Size Training,
CVPR18(9349-9358)
IEEE DOI 1812
Dictionaries, Training, Databases, Training data, Feature extraction, Machine learning BibRef

Jiang, Z., Zhu, X., Tan, W.t., Liston, R.,
Training sample selection for deep learning of distributed data,
ICIP17(2189-2193)
IEEE DOI 1803
Bandwidth, Distributed databases, Machine learning, Measurement, Neural networks, Training, Training data, Deep neural networks, training sample selection BibRef

Rahmani, M.[Mostafa], Atia, G.K.[George K.],
Robust and Scalable Column/Row Sampling from Corrupted Big Data,
RSL-CV17(1818-1826)
IEEE DOI 1802
Algorithm design and analysis, Clustering algorithms, Data models, Robustness, Sparse matrices BibRef

Dixit, M.[Mandar], Kwitt, R.[Roland], Niethammer, M.[Marc], Vasconcelos, N.M.[Nuno M.],
AGA: Attribute-Guided Augmentation,
CVPR17(3328-3336)
IEEE DOI 1711
Generation of artificial samples for training data. Data models, Neural networks, Object recognition, Training. BibRef

Paul, S.[Sujoy], Bappy, J.H.[Jawadul H.], Roy-Chowdhury, A.K.[Amit K.],
Non-uniform Subset Selection for Active Learning in Structured Data,
CVPR17(830-839)
IEEE DOI 1711
Data models, Entropy, Feature extraction, Labeling, Manuals, Uncertainty BibRef

Bappy, J.H.[Jawadul H.], Paul, S.[Sujoy], Tuncel, E.[Ertem], Roy-Chowdhury, A.K.[Amit K.],
The Impact of Typicality for Informative Representative Selection,
CVPR17(771-780)
IEEE DOI 1711
Activity recognition, Computational modeling, Context modeling, Data models, Entropy. Selection of training samples. BibRef

Tejada, J.[Javier], Alexandrov, M.[Mikhail], Skitalinskaya, G.[Gabriella], Stefanovskiy, D.[Dmitry],
Selection of Statistically Representative Subset from a Large Data Set,
CIARP16(476-483).
Springer DOI 1703
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Canévet, O.[Olivier], Fleuret, F.[François],
Large Scale Hard Sample Mining with Monte Carlo Tree Search,
CVPR16(5128-5137)
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find false positives. BibRef

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You Lead, We Exceed: Labor-Free Video Concept Learning by Jointly Exploiting Web Videos and Images,
CVPR16(923-932)
IEEE DOI 1612
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Masi, I.[Iacopo], Hassner, T.[Tal], Tran, A.T.[Anh Tuan], Medioni, G.[Gérard],
Rapid Synthesis of Massive Face Sets for Improved Face Recognition,
FG17(604-611)
IEEE DOI 1707
Cameras, Face, Face recognition, Rendering (computer graphics), Shape, Standards, BibRef

Masi, I.[Iacopo], Tran, A.T.[Anh Tuan], Hassner, T.[Tal], Leksut, J.T.[Jatuporn Toy], Medioni, G.[Gérard],
Do We Really Need to Collect Millions of Faces for Effective Face Recognition?,
ECCV16(V: 579-596).
Springer DOI 1611
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Richter, S.R.[Stephan R.], Vineet, V.[Vibhav], Roth, S.[Stefan], Koltun, V.[Vladlen],
Playing for Data: Ground Truth from Computer Games,
ECCV16(II: 102-118).
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Using game images for data. BibRef

Choi, M.K., Lee, H.G., Lee, S.C.,
Weighted SVM with classification uncertainty for small training samples,
ICIP16(4438-4442)
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Machine vision BibRef

Pourashraf, P.[Payam], Tomuro, N.[Noriko],
Use of a Large Image Repository to Enhance Domain Dataset for Flyer Classification,
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A Deeper Look at Dataset Bias,
GCPR15(504-516).
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Psutka, J.V.[Josef V.], Psutka, J.[Josef],
Sample Size for Maximum Likelihood Estimates of Gaussian Model,
CAIP15(II:462-469).
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Crowdsearching Training Sets for Image Classification,
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Xiao, T.[Tong], Xia, T.[Tian], Yang, Y.[Yi], Huang, C.[Chang], Wang, X.G.[Xiao-Gang],
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CVPR15(2691-2699)
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Shapovalov, R.[Roman], Vetrov, D.[Dmitry], Osokin, A.[Anton], Kohli, P.[Pushmeet],
Multi-utility Learning: Structured-Output Learning with Multiple Annotation-Specific Loss Functions,
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Schoeler, M.[Markus], Worgotter, F.[Florentin], Kulvicius, T.[Tomas], Papon, J.[Jeremie],
Unsupervised Generation of Context-Relevant Training-Sets for Visual Object Recognition Employing Multilinguality,
WACV15(805-812)
IEEE DOI 1503
Clutter; Context; Fasteners; Google; Nails; Search engines; Training BibRef

Desai, C.[Chaitanya], Eledath, J.[Jayan], Sawhney, H.[Harpreet], Bansal, M.[Mayank],
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WACV15(428-435)
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Chatzilari, E.[Elisavet], Nikolopoulos, S.[Spiros], Kompatsiaris, Y.F.[Yi-Fannis], Kittler, J.V.[Josef V.],
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ICIP14(4256-4260)
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Computational modeling BibRef

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ICCV13(2760-2767)
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ICIP13(3377-3380)
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active learning; labeling cost; object detection; sampling strategy BibRef

Li, W.B.[Wen-Bin], Fritz, M.[Mario],
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Ren, H.Y.[Hao-Yu], Hong, X.P.[Xiao-Peng], Heng, C.K.[Cher-Keng], Liang, L.H.[Lu-Hong], Chen, X.L.[Xi-Lin],
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Springer DOI 0509

See also Asymptotic properties of nearest neighbor rules using edited data. BibRef

Angelova, A.[Anelia], Abu-Mostafa, Y.[Yaser], Perona, P.[Pietro],
Pruning Training Sets for Learning of Object Categories,
CVPR05(I: 494-501).
IEEE DOI 0507
BibRef

Franco, A., Maltoni, D., Nanni, L.,
Reward-punishment editing,
ICPR04(IV: 424-427).
IEEE DOI 0409
Editing: remove patterns that are not classified correctly. (in the training set).
See also Asymptotic properties of nearest neighbor rules using edited data. BibRef

Kuhl, A., Kruger, L., Wohler, C., Kressel, U.,
Training of classifiers using virtual samples only,
ICPR04(III: 418-421).
IEEE DOI 0409
BibRef

Juszczak, P., Duin, R.P.W.,
Selective sampling based on the variation in label assignments,
ICPR04(III: 375-378).
IEEE DOI 0409
BibRef

Sprevak, D., Azuaje, F., Wang, H.,
A non-random data sampling method for classification model assessment,
ICPR04(III: 406-409).
IEEE DOI 0409
BibRef

Levin, A., Viola, P.A., Freund, Y.,
Unsupervised improvement of visual detectors using co-training,
ICCV03(626-633).
IEEE DOI 0311
Train detectors with limited data, then use that to label more data. Use training of 2 classifiers at once. Apply to vehicle tracking. BibRef

Kim, D.S.[Dong Sik], Lee, K.Y.[Kir-Yung],
Training sequence size in clustering algorithms and averaging single-particle images,
ICIP03(II: 435-438).
IEEE DOI 0312
BibRef

Johnson, A.Y., Sun, J.[Jie], Bobick, A.F.,
Using similarity scores from a small gallery to estimate recognition performance for larger galleries,
AMFG03(100-103).
IEEE DOI 0311
BibRef

Paredes, R., Vidal, E., Keysers, D.,
An evaluation of the WPE algorithm using tangent distance,
ICPR02(IV: 48-51).
IEEE DOI 0211
Weighted Prototype Editing. BibRef

Veeramachaneni, S.[Sriharsha], Nagy, G.[George],
Classifier Adaptation with Non-representative Training Data,
DAS02(123 ff.).
Springer DOI 0303
BibRef

Maletti, G., Ersbøll, B.K., Conradsen, K., Lira, J.,
An Initial Training Set Generation Scheme,
SCIA01(P-W3B). 0206
BibRef

Fursov, V.A.,
Training in Pattern Recognition from a Small Number of Observations Using Projections Onto Null-space,
ICPR00(Vol II: 785-788).
IEEE DOI 0009
BibRef

Miyamoto, T., Mitani, Y., Hamamoto, Y.,
Use of Bootstrap Samples in Quadratic Classifier Design,
ICPR00(Vol II: 789-792).
IEEE DOI 0009
BibRef

Mayer, H.A.[Helmut A.], Huber, R.[Reinhold],
ERC: Evolutionary Resample and Combine for Adaptive Parallel Training Data Set Selection,
ICPR98(Vol I: 882-885).
IEEE DOI 9808
BibRef

Takacs, B.[Barnabas], Sadovnik, L.[Lev], Wechsler, H.[Harry],
Optimal Training Set Design for 3D Object Recognition,
ICPR98(Vol I: 558-560).
IEEE DOI 9808
BibRef

Nedeljkovic, V., Milosavljevic, M.,
On the influence of the training set data preprocessing on neural networks training,
ICPR92(II:33-36).
IEEE DOI 9208
BibRef

Ferri, F.J., Vidal, E.,
Small sample size effects in the use of editing techniques,
ICPR92(II:607-610).
IEEE DOI 9208
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Small Sample Sizes Issues, Data analysis, Training Sets .


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