论文信息 - Fast incremental learning for one-class support vector classifier using sample margin information

Fast incremental learning for one-class support vector classifier using sample margin information

In this paper, we present a fast incremental one-class classifier algorithm for large scale problems. The proposed method reduces space and time complexities by reducing training set size during the training procedure using a criterion based on sample margin. After introducing the sample margin concept, we present the proposed algorithm and apply it to face detection database to show its efficiency and validity.

[1] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.

[2] Massimiliano Pontil,et al. Properties of Support Vector Machines , 1998, Neural Computation.

[3] David M. J. Tax,et al. Online SVM learning: from classification to data description and back , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

[4] M. M. Moya,et al. One-class classifier networks for target recognition applications , 1993 .

[5] Bernhard Schölkopf,et al. Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[6] Gert Cauwenberghs,et al. Incremental and Decremental Support Vector Machine Learning , 2000, NIPS.

[7] Klaus-Robert Müller,et al. Incremental Support Vector Learning: Analysis, Implementation and Applications , 2006, J. Mach. Learn. Res..