Fukunaga, K., and
Hayes, R.R.,
Effects of Sample Size in Classifier Design,
PAMI(11), No. 8, August 1989, pp. 873-885.
IEEE DOI
BibRef
8908
Raudys, S.J., and
Jain, A.K.,
Small Sample Size Effects in Statistical Pattern Recognition:
Recommendations for Practitioners,
PAMI(13), No. 3, March 1991, pp. 252-264.
IEEE DOI
BibRef
9103
Earlier:
Small sample size effects in statistical pattern recognition:
recommendations for practitioners and open problems,
ICPR90(I: 417-423).
IEEE DOI
9006
BibRef
Wong, A.K.C., and
Chiu, D.K.Y.,
Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data,
PAMI(9), No. 6, November 1987, pp. 796-805.
BibRef
8711
Chiu, D.K.Y.,
Wong, A.K.C., and
Chan, K.C.C.,
Synthesis of Statistical Knowledge from Time-Dependent Data,
PAMI(13), No. 3, March 1991, pp. 265-271.
IEEE DOI
BibRef
9103
Chan, S.C.,
Wong, A.K.C.,
Synthesis and recognition of sequences,
PAMI(13), No. 12, December 1991, pp. 1245-1255.
IEEE DOI
0401
BibRef
Ibrahim, J.,
Incomplete Data in Generalized Linear Models,
ASAJ(85), 1990, pp. 765-769.
BibRef
9000
Krishnan, T.,
Nandy, S.C.,
Efficiency of discriminant analysis when initial samples are classified
stochastically,
PR(23), No. 5, 1990, pp. 529-537.
Elsevier DOI
0401
BibRef
Benali, H.,
Buvat, I.,
Frouin, F.,
Bazin, J.P.,
di Paola, R.,
Foundations of Factor Analysis of Medical Image Sequences:
A Unified Approach and Some Practical Implications,
IVC(12), No. 6, July-August 1994, pp. 375-385.
Elsevier DOI
0401
BibRef
Lazo-Cortes, M.,
Ruiz-Shulcloper, J.,
Determining the Feature Relevance for Nonclassically Described Objects
and a New Algorithm to Compute Typical Fuzzy Testors,
PRL(16), No. 12, December 1995, pp. 1259-1265.
BibRef
9512
Lazo-Cortes, M.[Manuel],
Ruiz-Shulcloper, J.[Jose],
Alba-Cabrera, E.[Eduardo],
An Overview of the Evolution of the Concept of Testor,
PR(34), No. 4, April 2001, pp. 753-762.
Elsevier DOI
0101
BibRef
Sanchez-Díaz, G.[Guillermo],
Lazo-Cortés, M.[Manuel],
CT-EXT: An Algorithm for Computing Typical Testor Set,
CIARP07(506-514).
Springer DOI
0711
BibRef
Castelli, V.,
Cover, T.M.,
The Relative Value of Labeled and Unlabeled Samples in
Pattern-Recognition with an Unknown Mixing Parameter,
IT(42), No. 6, Part 2, November 1996, pp. 2102-2117.
9701
BibRef
Jain, A.K.[Anil K.],
Zongker, D.[Douglas],
Feature-Selection:
Evaluation, Application, and Small Sample Performance,
PAMI(19), No. 2, February 1997, pp. 153-158.
IEEE DOI
9703
Sequential Forward Floating Selection algorithm
(
See also Floating Search Methods in Feature-Selection. )
dominates other tested algorithms. Applied with 4 texture models on SAR.
BibRef
Zongker, D.,
Jain, A.K.,
Algorithms for Feature Selection: An Evaluation,
ICPR96(II: 18-22).
IEEE DOI
9608
(Michigan State Univ., USA)
BibRef
Raudys, S.J.[Sarunas J.],
Dimensionality, Sample-Size, and Classification Error of
Nonparametric Linear Classification Algorithms,
PAMI(19), No. 6, June 1997, pp. 667-671.
IEEE DOI
9708
BibRef
Earlier:
Linear Classifiers in Perceptron Design,
ICPR96(IV: 763-767).
IEEE DOI
9608
(Institute of Mathematics and Informatics, LIT)
BibRef
Tseng, C.H.,
Identification of Cubically Nonlinear Systems Using Undersampled Data,
VISP(144), No. 5, October 1997, pp. 267-277.
9806
BibRef
Skurichina, M.[Marina],
Duin, R.P.W.[Robert P.W.],
Regularisation of Linear Classifiers by Adding Redundant Features,
PAA(2), No. 1, 1999, pp. 44-52.
BibRef
9900
Hodgson, M.E.,
What Size Window for Image Classification: A Cognitive Perspective,
PhEngRS(64), No. 8, August 1998, pp. 797-807.
9808
BibRef
Guyon, I.[Isabelle],
Makhoul, J.[John],
Schwartz, R.[Richard],
Vapnik, V.[Vladimir],
What Size Test Set Gives Good Error Rate Estimates?,
PAMI(20), No. 1, January 1998, pp. 52-64.
IEEE DOI
9803
OCR. Applied to the character recognition problem.
BibRef
Avena, G.C.,
Ricotta, C.,
Volpe, F.,
The influence of principal component analysis on the spatial structure
of a multispectral dataset,
JRS(20), No. 17, November 1999, pp. 3367.
BibRef
9911
Sitek, A.,
Gullberg, G.T.,
Huesman, R.H.,
Correction for ambiguous solutions in factor analysis using a penalized
least squares objective,
MedImg(21), No. 3, March 2002, pp. 216-225.
IEEE Top Reference.
0205
BibRef
Ennaji, A.[Abdellatif],
Ribert, A.[Arnaud],
Lecourtier, Y.[Yves],
From data topology to a modular classifier,
IJDAR(6), No. 1, 2003, pp. 1-9.
Springer DOI
0308
BibRef
Ribert, A.,
Ennaji, A.,
Lecourtier, Y.,
Clustering Data: Dealing with High Density Variations,
ICPR00(Vol II: 736-739).
IEEE DOI
0009
BibRef
Stocker, E.,
Ribert, A.,
Lecourtier, Y.,
Ennaji, A.,
Incremental Distributed Classifier Building,
ICPR96(IV: 128-132).
IEEE DOI
9608
(Univ. de Rouen, F)
BibRef
Liu, Z.Y.,
Chiu, K.C.,
Xu, L.,
Investigations on Non-Gaussian Factor Analysis,
SPLetters(11), No. 7, July 2004, pp. 597-600.
IEEE Abstract.
0407
BibRef
Wang, Y.[Ye],
Huang, S.T.[Shang-Teng],
Training TSVM with the proper number of positive samples,
PRL(26), No. 14, 15 October 2005, pp. 2187-2194.
Elsevier DOI
0510
Transductive Support Vector Machine.
BibRef
Knijnenburg, T.A.[Theo A.],
Reinders, M.J.T.[Marcel J.T.],
Wessels, L.F.A.[Lodewyk F.A.],
Artifacts of Markov blanket filtering based on discretized features in
small sample size applications,
PRL(27), No. 7, May 2006, pp. 709-714.
Elsevier DOI
0604
Feature evaluation and selection. Apply to gene expression data.
BibRef
Lai, C.[Carmen],
Reinders, M.J.T.[Marcel J.T.],
Wessels, L.F.A.[Lodewyk F.A.],
Random subspace method for multivariate feature selection,
PRL(27), No. 10, 15 July 2006, pp. 1067-1076.
Elsevier DOI
0606
Random subspace method; Small sample size problem
BibRef
Li, Y.L.[Yun-Lei],
Wessels, L.F.A.[Lodewyk F.A.],
de Ridder, D.[Dick],
Reinders, M.J.T.[Marcel J.T.],
Classification in the presence of class noise using a probabilistic
Kernel Fisher method,
PR(40), No. 12, December 2007, pp. 3349-3357.
Elsevier DOI
0709
BibRef
And: Erratum:
PR(41), No. 3, March 2008, pp. 1214.
Elsevier DOI
0711
Classification; Class noise; Labeling noise; Kernel Fisher discriminant
BibRef
Liang, Y.X.[Yi-Xiong],
Li, C.R.[Cheng-Rong],
Gong, W.G.[Wei-Guo],
Pan, Y.J.[Ying-Jun],
Uncorrelated linear discriminant analysis based on weighted pairwise
Fisher criterion,
PR(40), No. 12, December 2007, pp. 3606-3615.
Elsevier DOI
0709
Uncorrelated LDA; Null space LDA; Weighted pairwise Fisher criterion;
Decorrelation
BibRef
Isaksson, A.,
Wallman, M.,
Goransson, H.,
Gustafsson, M.G.,
Cross-validation and bootstrapping are unreliable in small sample
classification,
PRL(29), No. 14, October 2008, pp. 1960-1965.
Elsevier DOI
0804
Supervised classification; Performance estimation; Confidence interval
BibRef
Das, K.[Koel],
Nenadic, Z.[Zoran],
An efficient discriminant-based solution for small sample size problem,
PR(42), No. 5, May 2009, pp. 857-866.
Elsevier DOI
0902
Feature extraction; Principal component analysis; Classification;
Linear discriminant analysis; Bayes error
BibRef
Jacquemont, S.[Stephanie],
Jacquenet, F.[Francois],
Sebban, M.[Marc],
A lower bound on the sample size needed to perform a significant
frequent pattern mining task,
PRL(30), No. 11, 1 August 2009, pp. 960-967.
Elsevier DOI
0909
Frequent pattern mining; Lower bound
BibRef
Hernandez-Leal, P.[Pablo],
Carrasco-Ochoa, J.A.[J. Ariel],
Martínez-Trinidad, J.F.,
Olvera-Lopez, J.A.[J. Arturo],
InstanceRank based on borders for instance selection,
PR(46), No. 1, January 2013, pp. 365-375.
Elsevier DOI
1209
Instance selection; Instance ranking; Border instances; Supervised
classification
BibRef
Olvera-López, J.A.[J. Arturo],
Martínez-Trinidad, J.F.[J. Francisco],
Carrasco-Ochoa, J.A.[J. Ariel],
Mixed Data Object Selection Based on Clustering and Border Objects,
CIARP07(674-683).
Springer DOI
0711
Instance selection.
BibRef
Hernandez-Rodriguez, S.[Selene],
Martínez-Trinidad, J.F.[J. Francisco],
Carrasco-Ochoa, J.A.[J. Ariel],
On the selection of base prototypes for LAESA and TLAESA classifiers,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Macià, N.[Núria],
Bernadó-Mansilla, E.[Ester],
Orriols-Puig, A.[Albert],
Ho, T.K.[Tin Kam],
Learner excellence biased by data set selection:
A case for data characterisation and artificial data sets,
PR(46), No. 3, March 2013, pp. 1054-1066.
Elsevier DOI
1212
BibRef
Earlier: A1, A2, A3:
Preliminary approach on synthetic data sets generation based on class
separability measure,
ICPR08(1-4).
IEEE DOI
0812
Supervised learning; Learner assessment; Data complexity
BibRef
Macià, N.[Núria],
Ho, T.K.[Tin Kam],
Orriols-Puig, A.[Albert],
Bernadó-Mansilla, E.[Ester],
The Landscape Contest at ICPR 2010,
ICPR-Contests10(29-45).
Springer DOI
1008
Evaluate robustness of supervised classifications and their limitations.
BibRef
Hanczar, B.[Blaise],
Dougherty, E.R.[Edward R.],
The reliability of estimated confidence intervals for classification
error rates when only a single sample is available,
PR(46), No. 3, March 2013, pp. 1067-1077.
Elsevier DOI
1212
Supervised learning; Error estimation; High dimension; Small sample
setting; Confidence interval
BibRef
Jiang, Y.G.[Yu-Gang],
Wang, J.[Jun],
Xue, X.,
Chang, S.F.[Shih-Fu],
Query-Adaptive Image Search With Hash Codes,
MultMed(15), No. 2, 2013, pp. 442-453.
IEEE DOI
1302
BibRef
Jiang, Y.G.[Yu-Gang],
Wang, J.[Jun],
Chang, S.F.[Shih-Fu],
Lost in binarization: query-adaptive ranking for similar image search
with compact codes,
ICMR11(16).
DOI Link
1301
BibRef
And: A2, A1, A3:
Label diagnosis through self tuning for web image search,
CVPR09(1390-1397).
IEEE DOI
0906
Are the initial label good?
BibRef
Wu, H.[Hao],
Miao, Z.J.[Zhen-Jiang],
Wang, Y.[Yi],
Lin, M.[Manna],
Optimized recognition with few instances based on semantic distance,
VC(31), No. 4, April 2015, pp. 367-375.
Springer DOI
1503
Learning with only a few examples.
BibRef
Hsiao, P.H.[Pai-Heng],
Chang, F.J.[Feng-Ju],
Lin, Y.Y.[Yen-Yu],
Learning Discriminatively Reconstructed Source Data for Object
Recognition With Few Examples,
IP(25), No. 8, August 2016, pp. 3518-3532.
IEEE DOI
1608
learning systems
BibRef
Potapov, A.[Alexey],
Potapova, V.[Vita],
Peterson, M.[Maxim],
A feasibility study of an autoencoder meta-model for improving
generalization capabilities on training sets of small sizes,
PRL(80), No. 1, 2016, pp. 24-29.
Elsevier DOI
1609
Autoencoders
BibRef
Li, D.[Dong],
Liu, S.L.[Shu-Lin],
Zhang, H.L.[Hong-Li],
A method of anomaly detection and fault diagnosis with online
adaptive learning under small training samples,
PR(64), No. 1, 2017, pp. 374-385.
Elsevier DOI
1701
Artificial immune system
BibRef
Kuncheva, L.I.[Ludmila I.],
Rodríguez, J.J.[Juan J.],
On feature selection protocols for very low-sample-size data,
PR(81), 2018, pp. 660-673.
Elsevier DOI
1806
Feature selection, Wide datasets, Experimental protocol,
Training/testing, Cross-validation
BibRef
Ustuner, M.,
Sanli, F.B.,
Abdikan, S.,
Balanced Vs Imbalanced Training Data: Classifying Rapideye Data With
Support Vector Machines,
ISPRS16(B7: 379-384).
DOI Link
1610
BibRef
Su, H.[Hang],
Yin, Z.Z.[Zhao-Zheng],
Kanade, T.[Takeo],
Huh, S.[Seungil],
Active sample selection and correction propagation on a
gradually-augmented graph,
CVPR15(1975-1983)
IEEE DOI
1510
BibRef
Kadar, I.[Ilan],
Ben-Shahar, O.[Ohad],
Small sample scene categorization from perceptual relations,
CVPR12(2711-2718).
IEEE DOI
1208
BibRef
Singh, M.[Mayank],
Gupta, P.K.,
Mishra, S.[Shailendra],
Automated test data generation for mutation testing using AspectJ
programs,
ICIIP11(1-5).
IEEE DOI
1112
BibRef
Xia, X.T.[Xin-Tao],
Zhou, Q.[Qing],
Zhu, J.M.[Jian-Min],
Evaluation for repeatability and reproducibility of information poor
process,
IASP10(528-531).
IEEE DOI
1004
Incomplete, small sample size.
BibRef
Petersen, H.[Henry],
Poon, J.[Josiah],
Reworking Bridging for Use within the Image Domain,
CAIP09(832-839).
Springer DOI
0909
Bridging: from string text classification (Zelikovitz et al.)
Data set issues.
BibRef
Ricamato, M.T.[Maria Teresa],
Marrocco, C.[Claudio],
Tortorella, F.[Francesco],
MCS-based balancing techniques for skewed classes:
An empirical comparison,
ICPR08(1-4).
IEEE DOI
0812
Train multiple classifiers using the minority class and part of majority class.
BibRef
Na, J.H.[Jin Hee],
Yun, S.M.[Seok Min],
Kim, M.S.[Min-Soo],
Choi, J.Y.[Jin Young],
Relevant pattern selection for subspace learning,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Xu, Y.[Yong],
Zhang, D.[David],
A New Solution Scheme of Unsupervised Locality Preserving Projection
Method for the SSS Problem,
SSPR08(775-781).
Springer DOI
0812
Small Sample Size problem.
BibRef
Morales-Manilla, L.R.[Luis Roberto],
Sanchez-Diaz, G.[Guillermo],
FS-EX Plus: A New Algorithm for the Calculation of Typical FS-Testor
Set,
CIARP07(380-386).
Springer DOI
0711
Feature Selection.
BibRef
Zheng, Y.J.[Yu-Jie],
Yang, J.Y.[Jing-Yu],
Yang, J.[Jian],
Wu, X.J.[Xiao-Jun],
Effective classification image space which can solve small sample size
problem,
ICPR06(II: 861-864).
IEEE DOI
0609
BibRef
Pranckeviciene, E.[Erinija],
Ho, T.K.[Tin Kam],
Somorjai, R.[Ray],
Class Separability in Spaces Reduced By Feature Selection,
ICPR06(III: 254-257).
IEEE DOI
0609
BibRef
Zhu, X.Q.[Xing-Quan],
Wu, X.D.[Xin-Dong],
Scalable Representative Instance Selection and Ranking,
ICPR06(III: 352-355).
IEEE DOI
0609
BibRef
Xuan, G.R.[Guo-Rong],
Zhu, X.M.[Xiu-Ming],
Chai, P.Q.[Pei-Qi],
Zhang, Z.P.[Zhen-Ping],
Shi, Y.Q.[Yun Q.],
Fu, D.D.[Dong-Dong],
Feature Selection based on the Bhattacharyya Distance,
ICPR06(III: 1232-1235).
IEEE DOI
0609
BibRef
And:
ICPR06(IV: 957).
IEEE DOI
0609
BibRef
Levi, K.[Kobi],
Fink, M.[Michael],
Weiss, Y.[Yair],
Learning From a Small Number of Training Examples by Exploiting Object
Categories,
LCV04(96).
IEEE DOI
0406
BibRef
Ulusoy, I.[Ilkay],
Bishop, C.M.[Christopher M.],
Comparison of Generative and Discriminative Techniques for Object
Detection and Classification,
CLOR06(173-195).
Springer DOI
0711
BibRef
Earlier:
Generative versus Discriminative Methods for Object Recognition,
CVPR05(II: 258-265).
IEEE DOI
0507
BibRef
Salah, A.A.,
Alpaydin, E.,
Incremental mixtures of factor analysers,
ICPR04(I: 276-279).
IEEE DOI
0409
BibRef
Huang, R.[Rui],
Liu, Q.S.[Qing-Shan],
Lu, H.Q.[Han-Qing],
Ma, S.D.[Song-De],
Solving the small sample size problem of LDAf,
ICPR02(III: 29-32).
IEEE DOI
0211
BibRef
Duin, R.P.W.[Robert P.W.],
Relational Discriminant Analysis and its Large Sample Size Problem,
ICPR98(Vol I: 445-449).
IEEE DOI
9808
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Unbalanced Datasets, Imbalanced Sample Sizes, Imbalanced Data .