Support Vector Machines work by mapping training data for classification tasks into a high dimensional feature space. In the feature space they then find a maximal margin hyperplane which separates the data. This hyperplane is usually found using a quadratic programming routine which is computationally intensive, and is non trivial to implement. In this paper we propose an adaptation of the Adatron algorithm for classification with kernels in high dimensional spaces. The algorithm is simple and can find a solution very rapidly with an exponentially fast rate of convergence (in the number of iterations) towards the optimal solution. Experimental results with real and artificial datasets are provided. Keywords: Support Vector Machine, Large Margin Classifier, Adatron, Statistical Mechanics 1 INTRODUCTION Support Vector (SV) machines are an algorithm introduced by Vapnik and co-workers [5, 4] theoretically motivated by VC theory. They are based on the following idea: input points are ma...
|Translated title of the contribution||The Kernel-Adatron : A fast and simple learning procedure for support vector machines|
|Title of host publication||ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning|
|Editors||Jude W Shavlik|
|Publisher||Association for Computing Machinery (ACM)|
|Pages||188 - 196|
|Number of pages||9|
|Publication status||Published - 1998|