This chapter describes an active-set algorithm for the solution of quadratic programming problems in the context of Support Vector Machines (SVMs). Most of the common SVM optimizers implement working-set algorithms like the SMO method because of their ability to handle large data sets. Although they show generally good results, they may perform weakly in some situations, e.g., if the problem is...

This paper reports on a rough set approach to classifying meteorological volumetric radar data used to detect storm events responsible for summer severe weather. The classification of storm cells is a difficult problem due to the complex evolution of storm cells, the high dimensionality of the weather data, and the imprecision and incompleteness of the data. A rough set approach is used to clas...

Rough set theory is introduced into linear υ support vector machine (svm), and rough marginbased linear υ svm is proposed in this paper. By constructing rough lower margin, rough upper margin and rough boundary in linear υ svm, then we maximize the rough margin not margin in linear υ svm. Thus more points are considered in constructing the separating hyper-plane than those used in linear υ svm....

In this paper, we propose a Web Decision Support System for Impacts Assessment of Urban Mobility (WDSS4IA) which is based on a new approach. The latter uses fuzzy set theory for modeling criteria, belief theory for evaluations fusion from various information sources and it is able to handle uncertainty and vagueness. In this paper, the WDSS4IA is presented, focusing on its conception, modules a...

The methods for extending binary support vectors machines (SVMs) can be broadly divided into two categories, namely, 1-v-r (one versus rest) and 1-v-1 (one versus one). The 1-v-r approach tends to have higher training time, while 1-v-1 approaches tend to create a large number of binary classifiers that need to be analyzed and stored during the operational phase. This paper describes how rough s...

In this work, a Genetic Algorithm boosted Least Square Support Vector Machine model by a set of linear equations instead of a quadratic program, which is improved version of Support Vector Machine model, was used for estimation of 98 pure compounds second virial coefficient. Compounds were classified to the different groups. Finest parameters were obtained by Genetic Algorithm method ...