Support vector machine is a special kind of learning machines, proposed by Vapnik. The learning capability of support vector machines depends on the Vapnik-Chervonenkis dimension of the kernel function used. In this paper we construct a new kernel function for support vector machine, which is based on Walsh functions. We prove some theoretical results related to the VC-dimension of the support ...

In the recent few decades there has been very significant developments in the theoretical understanding of Support vector machines (SVMs) as well as algorithmic strategies for implementing them, and applications of the approach to practical problems. SVMs introduced by Vapnik and others in the early 1990s are machine learning systems that utilize a hypothesis space of linear functions in a high...

The core problem of statistics and machine learning addresses the question how can we efficiently find a statistical model to describe empirical data. Classical statistical approaches to solve this problem have been complemented during the last 15 years by Neural Computation, a very promising strategy to data analysis. The Dagstuhl seminar on “Inference Principles and Model Selection” — the fou...

in this paper we have proposed an approach for emotion detection in implicit texts. we have introduced a combinational system based on three subsystems. each one analyzes input data from a different aspect and produces an emotion label as output. the first subsystem is a machine learning method. the second one is a statistical approach based on vector space model (vsm) and the last one is a key...

This report describes a recent bound on expected support vector machine (SVM) generalization error [3] and frames this work in the context of computational learning theory [1], discussing the practical value of these bounds and the value to our mathematical understanding of machine learning. The fundamentals of computational learning theory are first outlined: PAC learning is defined, an exampl...