A simple learning algorithm for maximal margin classi ers (also support vector machines with quadratic cost function) is proposed. We build our iterative algorithm on top of the Schlesinger–Kozinec algorithm (S–K-algorithm) from 1981 which nds a maximal margin hyperplane with a given precision

Random sampling techniques have been developed in for geometric/combinatorial optimization problems; see, e.g., [Cla88, Cla95, AS93, GW99]. In this note, we apply one of these techniques for obtaining (hopefully) efficient support vector machine training algorithm. In particular, we propose one way to find "outliers" by using the sampling technique.

We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like Vapnik 's maximal-margin classifier, our algorithm takes advantage of data that are linearly separable

The maximal margin classifier is similar to the perceptron: • It also assumes that the data points are linearly separable • It aims at finding the separating hyperplane with the maximal geometric margin (not just anyone- typical of a perceptron) x 2

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters

Abstract. We introduce and analyze a new algorithm for linear classification which combines Rosenblatt’s perceptron algorithm with Helmbold and Warmuth’s leave-one-out method. Like Vapnik’s maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large

Abstract. We introduce and analyze a new algorithm for linear classification which combines Rosenblatt’s perceptron algorithm with Helmbold and Warmuth’s leave-one-out method. Like Vapnik’s maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large

Abstract. We introduce and analyze a new algorithm for linear classification which combines Rosenblatt’s perceptron algorithm with Helmbold and Warmuth’s leave-one-out method. Like Vapnik’s maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large

Maximal margin classifiers are a core technology in modern machine learning. They have strong theoretical justifications and have shown empirical successes. We provide an alternative justification for maximal margin hyperplane classifiers by relating them to Bayes optimal classifiers that use

In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from