Support Vector Machines, SVM, One-Class Classification

Chapter Contents (Back)
Support Vector Machines. SVM. One Class.

Rätsch, G.[Gunnar], Mika, S.[Sebastian], Schölkopf, B.[Bernhard], Müller, K.R.[Klaus-Robert],
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification,
PAMI(24), No. 9, September 2002, pp. 1184-1199.
IEEE Abstract. 0209
Equivalence of SVM ( See also Support Vector Machines. ) and boosting-like algorithm ( See also Boosting Performance in Neural Networks. ). BibRef

Camastra, F.[Francesco], Verri, A.[Alessandro],
A Novel Kernel Method for Clustering,
PAMI(27), No. 5, May 2005, pp. 801-804.
IEEE Abstract. 0501
Inspired by k-Means, iterative refinement of culster by a one-class SVM. BibRef

Tran, Q.A.[Quang-Anh], Li, X.[Xing], Duan, H.X.[Hai-Xin],
Efficient performance estimate for one-class support vector machine,
PRL(26), No. 8, June 2005, pp. 1174-1182.
Elsevier DOI 0506

Muñoz, A.[Alberto], Moguerza, J.M.[Javier M.],
Estimation of High-Density Regions Using One-Class Neighbor Machines,
PAMI(28), No. 3, March 2006, pp. 476-480.

Camci, F.[Fatih], Chinnam, R.B.[Ratna Babu],
General support vector representation machine for one-class classification of non-stationary classes,
PR(41), No. 10, October 2008, pp. 3021-3034.
Elsevier DOI 0808
Novelty detection; One-class classification; Support vector machine; Non-stationary classes; Non-stationary processes; Online training; Outlier detection BibRef

Choi, Y.S.[Young-Sik],
Least squares one-class support vector machine,
PRL(30), No. 13, 1 October 2009, pp. 1236-1240.
Elsevier DOI 0909
LS (least squares) one-class SVM; Proximity measure; Relevance ranking; One-class SVM (support vector machine) BibRef

Munoz-Mari, J., Bovolo, F., Gomez-Chova, L., Bruzzone, L., Camp-Valls, G.,
Semisupervised One-Class Support Vector Machines for Classification of Remote Sensing Data,
GeoRS(48), No. 8, August 2010, pp. 3188-3197.

Hansen, M.S.[Michael Sass], Sjostrand, K.[Karl], Larsen, R.[Rasmus],
On the regularization path of the support vector domain description,
PRL(31), No. 13, 1 October 2010, pp. 1919-1923.
Elsevier DOI 1003
Support vector domain description (SVDD); Regularization path; One-class classifier; Novelty detection BibRef

Hansen, M.S.[Michael Sass], Sjöstrand, K.[Karl], Ólafsdóttir, H.[Hildur], Larsson, H.B.W.[Henrik B. W.], Stegmann, M.B.[Mikkel B.], Larsen, R.[Rasmus],
Robust Pseudo-hierarchical Support Vector Clustering,
Springer DOI 0706

Huang, G.X.[Guang-Xin], Chen, H.[Huafu], Zhou, Z.L.[Zhong-Li], Yin, F.[Feng], Guo, K.[Ke],
Two-class support vector data description,
PR(44), No. 2, February 2011, pp. 320-329.
Elsevier DOI 1011
Support vector data description; D-SVDD; TC-SVDD; One-class classification BibRef

Bilgin, G., Erturk, S., Yildirim, T.,
Segmentation of Hyperspectral Images via Subtractive Clustering and Cluster Validation Using One-Class Support Vector Machines,
GeoRS(49), No. 8, August 2011, pp. 2936-2944.

Li, Y.H.[Yu-Hua],
Selecting training points for one-class support vector machines,
PRL(32), No. 11, 1 August 2011, pp. 1517-1522.
Elsevier DOI 1108
One-class support vector machines; Training set selection; Extreme points; Novelty detection BibRef

Tohmé, M.[Mireille], Lengellé, R.[Régis],
Maximum Margin One Class Support Vector Machines for multiclass problems,
PRL(32), No. 13, 1 October 2011, pp. 1652-1658.
Elsevier DOI 1109
Multiclass; Support Vector Machines; Classification; Detection BibRef

Cyganek, B.[Boguslaw],
One-Class Support Vector Ensembles for Image Segmentation and Classification,
JMIV(42), No. 2-3, February 2012, pp. 103-117.
WWW Link. 1202

Lian, H.[Heng],
On feature selection with principal component analysis for one-class SVM,
PRL(33), No. 9, 1 July 2012, pp. 1027-1031.
Elsevier DOI 1202
Dimension reduction; Image retrieval; Support vector machine BibRef

Junejo, I.N.[Imran N.], Bhutta, A.A.[Adeel A.], Foroosh, H.[Hassan],
Single-class SVM for dynamic scene modeling,
SIViP(7), No. 1, January 2013, pp. 45-52.
WWW Link. 1301

Jaberi, M.[Maryam], Pensky, M.[Marianna], Foroosh, H.[Hassan],
SWIFT: Sparse Withdrawal of Inliers in a First Trial,
reduces the problem to sampling an extremely sparse subset of the original population of data in one grab. edge, line detection or multi-body structure from motion. BibRef

di Martino, M.[Matias], Hernandez, G.[Guzman], Fiori, M.[Marcelo], Fernandez, A.[Alicia],
A new framework for optimal classifier design,
PR(46), No. 8, August 2013, pp. 2249-2255.
Elsevier DOI 1304
Class imbalance; One class SVM; F-measure; Recall; Precision; Fraud detection; Level set method BibRef

Fiori, M.[Marcelo], di Martino, M.[Matias], Fernández, A.[Alicia],
An optimal multiclass classifier design,
Algorithm design and analysis, Level set, Nickel, Optimization, Support vector machines, Testing, Training BibRef

Valdés, M.[Matías], Fiori, M.[Marcelo],
Re-Weighted L_1 Algorithms within the Lagrange Duality Framework,
Springer DOI 1910

Lee, C.[Changki],
Pegasos Algorithm for One-Class Support Vector Machine,
IEICE(E96-D), No. 5, May 2013, pp. 1223-1226.
WWW Link. 1305

Di Martino, M.[Matías], Fernández, A.[Alicia], Iturralde, P.[Pablo], Lecumberry, F.[Federico],
Novel classifier scheme for imbalanced problems,
PRL(34), No. 10, 15 July 2013, pp. 1146-1151.
Elsevier DOI 1306
Class imbalance; One class SVM; F-measure; Recall; Precision; Fraud detection BibRef

Khan, N.M.[Naimul Mefraz], Ksantini, R.[Riadh], Ahmad, I.S.[Imran Shafiq], Guan, L.[Ling],
Covariance-guided One-Class Support Vector Machine,
PR(47), No. 6, 2014, pp. 2165-2177.
Elsevier DOI 1403
Covariance BibRef

Jumutc, V., Suykens, J.A.K.[Johan A.K.],
Multi-Class Supervised Novelty Detection,
PAMI(36), No. 12, December 2014, pp. 2510-2523.
Algorithm design and analysis BibRef

Xiao, Y., Wang, H., Xu, W.,
Parameter Selection of Gaussian Kernel for One-Class SVM,
Cyber(45), No. 5, May 2015, pp. 927-939.
Cybernetics BibRef

Dufrenois, F., Noyer, J.C.,
One class proximal support vector machines,
PR(52), No. 1, 2016, pp. 96-112.
Elsevier DOI 1601
Outlier detection BibRef

Liu, Y.[Yong], Zhang, B.[Biling], Chen, B.[Bin], Yang, Y.D.[Yan-Dong],
Robust solutions to fuzzy one-class support vector machine,
PRL(71), No. 1, 2016, pp. 73-77.
Elsevier DOI 1602
One-class SVM BibRef

Erfani, S.M.[Sarah M.], Rajasegarar, S.[Sutharshan], Karunasekera, S.[Shanika], Leckie, C.[Christopher],
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning,
PR(58), No. 1, 2016, pp. 121-134.
Elsevier DOI 1606
Anomaly detection BibRef

Wu, T.[Tong], Liang, Y.C.[Yan-Chun], Varela, R.[Ramiro], Wu, C.G.[Chun-Guo], Zhao, G.Z.[Guo-Zhong], Han, X.S.[Xiao-Song],
Self-adaptive SVDD integrated with AP clustering for one-class classification,
PRL(84), No. 1, 2016, pp. 232-238.
Elsevier DOI 1612
One-class classifier BibRef

Xiao, Y.C.[Ying-Chao], Wang, H.G.[Huan-Gang], Xu, W.L.[Wen-Li],
Ramp Loss based robust one-class SVM,
PRL(85), No. 1, 2017, pp. 15-20.
Elsevier DOI 1612
Ramp Loss function BibRef

Wang, S.[Siqi], Liu, Q.A.[Qi-Ang], Zhu, E.[En], Porikli, F.M.[Fatih M.], Yin, J.P.[Jian-Ping],
Hyperparameter selection of one-class support vector machine by self-adaptive data shifting,
PR(74), No. 1, 2018, pp. 198-211.
Elsevier DOI 1711
One-class SVM BibRef

Xue, Y.J.[Yong-Jian], Beauseroy, P.[Pierre],
Transfer learning for one class SVM adaptation to limited data distribution change,
PRL(100), No. 1, 2017, pp. 117-123.
Elsevier DOI 1712
Multi-task learning for one-class SVM with additional new features,
Transfer learning. Engines, Feature extraction, Kernel, Sensors, Support vector machines, Training, Training data, evolving feature space, multi-task learning, one-class SVM, outliers, detection BibRef

Zhang, W.[Wei], Du, L.[Lan], Li, L.[Liling], Zhang, X.F.[Xue-Feng], Liu, H.W.[Hong-Wei],
Infinite Bayesian one-class support vector machine based on Dirichlet process mixture clustering,
PR(78), 2018, pp. 56-78.
Elsevier DOI 1804
Dirichlet process mixture, One-class classifiers, One-class support vector machine, Gibbs sampling BibRef

Sadooghi, M.S.[Mohammad Saleh], Khadem, S.E.[Siamak Esmaeilzadeh],
Improving one class support vector machine novelty detection scheme using nonlinear features,
PR(83), 2018, pp. 14-33.
Elsevier DOI 1808
Novelty detection, OC-SVM, Nonlinear feature, Wavelet, Bearing vibration signal, Entropy BibRef

Xing, H.J.[Hong-Jie], Ji, M.[Man],
Robust one-class support vector machine with rescaled hinge loss function,
PR(84), 2018, pp. 152-164.
Elsevier DOI 1809
One-class classification, One-class support vector machine, Hinge loss function, Half-quadratic optimization BibRef

Kefi-Fatteh, T.[Takoua], Ksantini, R.[Riadh], Kaâniche, M.B.[Mohamed-Bécha], Bouhoula, A.[Adel],
A novel incremental one-class support vector machine based on low variance direction,
PR(91), 2019, pp. 308-321.
Elsevier DOI 1904
One-Class classification, Incremental learning, Support vector machine, Low variance directions BibRef

Vetrova, V., Coup, S., Frank, E., Cree, M.J.,
Hidden Features: Experiments with Feature Transfer for Fine-Grained Multi-Class and One-Class Image Categorization,
Feature extraction, Task analysis, Convolutional neural networks, Training, Tuning, Support vector machines, Image recognition, Siamese networks BibRef

Fragoso, V., Scheirer, W., Hespanha, J., Turk, M.,
One-class slab support vector machine,
Kernel, Optimization, Retina, Slabs, Support vector machines, Training, Visualization BibRef

Krawczyk, B.[Bartosz], Wozniak, M.[Michal], Cyganek, B.[Boguslaw],
Weighted One-Class Classifier Ensemble Based on Fuzzy Feature Space Partitioning,
Accuracy BibRef

Braun, A.[Andreas],
Evaluation of One-Class SVM for Pixel-Based and Segment-Based Classification in Remote Sensing,
PDF File. 1009

Kim, P.J.[Pyo Jae], Chang, H.J.[Hyung Jin], Choi, J.Y.[Jin Young],
Fast incremental learning for one-class support vector classifier using sample margin information,

Labusch, K.[Kai], Timm, F.[Fabian], Martinetz, T.[Thomas],
Simple Incremental One-Class Support Vector Classification,
Springer DOI 0806

Martinetz, T.[Thomas],
MinOver Revisited for Incremental Support-Vector-Classification,
Springer DOI 0505

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Support Vector Machines, SVM, Surveys, Reviews, General .

Last update:Mar 29, 2020 at 12:14:19