PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Incremental Support Vector Learning: Analysis, Implementation and Applications
Pavel Laskov, Christian Gehl, Stefan Krüger and Klaus-Robert Müller
Journal of Machine Learning Research 2005.


Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1582
Deposited By:Pavel Laskov
Deposited On:28 November 2005