Virtual Examples for Text Classification with Support Vector Machines

In this study variants of SSVM (spline SSVM, piecewise polynomial SSVM) are proposed for high- dimensional classification.. Theoretical results demonstrate piecewise polynomial

This research work adopts Support Vector Machines (SVMs) as pattern classification techniques which are based on iris code model which the feature vector size is transformed

This paper explores the effectiveness of support vector machines (SVMs), in tackling the problem of question classification into Bloom's cognitive levels.. To do so, a dataset

In this following of the section, we describe the proposed SVM-PSO classification system for the classification of high-dimensional data. As mentioned in the Introduction,

classification of poetry based on theme; and classification of either poetry or non-poetry based on the shape structure and tfidf features.. The dataset for

While regular SVM perform the entire training in one - time consuming - optimization step, multilevel SVMs first build a hierarchy of prob- lems decreasing in size that resemble

Comparison of Support Vector sets and performance for training on the original database and training on the generated Virtual Support Vectors.. In both training runs, we used

Three approaches have been proposed to solve survival problems using SVMs: the regression (Shiv- aswamy et al., 2007), the ranking (Van Belle et al., 2007; Evers and Messow, 2008;