A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
On the Learnability and Design of Output Codes for Multiclass Problems
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Neural Computation
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We propose a newalgorithm for the categorisation of data into multiple classes. It minimises a quadratic homogeneous program, and can be viewed as a generalisation of the well known support vector machines to multiple classes. For only one class it reduces to a quadratic problem, whose solution can be seen as an estimate of the support of a distribution. Given a set of labelled data, our algorithm estimates for each class a representative vector in a feature space. Each of these vectors is expressible as a linear combination of the training data in its class, mapped into feature space. Therefore our algorithm needs less parameters than other multi-class support vector approaches.