A New Approximate Maximal Margin Classification Algorithm

The Role of Weight Shrinking in Large Margin Perceptron Learning

We introduce into the classical perceptron algorithm with margin a mechanism that shrinks the current weight vector as a first step of the update. If the shrinking factor is constant the resulting algorithm may be regarded as a margin-error-driven version of NORMA with constant learning rate. In this case we show that the allowed strength of shrinking depends on the value of the maximum margin....

Perceptron-like large margin classifiers

We consider perceptron-like algorithms with margin in which the standard classification condition is modified to require a specific value of the margin in the augmented space. The new algorithms are shown to converge in a finite number of steps and used to approximately locate the optimal weight vector in the augmented space following a procedure analogous to Bolzano’s bisection method. We demo...

Exact and approximate solutions of fuzzy LR linear systems: New algorithms using a least squares model and the ABS approach

We present a methodology for characterization and an approach for computing the solutions of fuzzy linear systems with LR fuzzy variables. As solutions, notions of exact and approximate solutions are considered. We transform the fuzzy linear system into a corresponding linear crisp system and a constrained least squares problem. If the corresponding crisp system is incompatible, then the fuzzy ...

A New Approximate Maximal Margin Classi ationAlgorithmClaudio

Maximal Margin Classification for Metric Spaces

In this article we construct a maximal margin classification algorithm for arbitrary metric spaces. At first we show that the Support Vector Machine (SVM) is a maximal margin algorithm for the class of metric spaces where the negative squared distance is conditionally positive definite (CPD). This means that the metric space can be isometrically embedded into a Hilbert space, where one performs...