Quadratic optimization fine tuning for the learning phase of SVM

Authors:
Miguel González-Mendoza;Neil Hernández-Gress;André Titli
Affiliations:
LAAS-CNRS, Toulouse Cedex 4, France;ITESM-CEM, Atizapán de Zaragoza, Estado de México, México;INSA Toulouse, Toulouse Cedex 4, France
Venue:
ISSADS'05 Proceedings of the 5th international conference on Advanced Distributed Systems
Year:
2005

Citing 5
Cited 1

Solving the quadratic programming problem arising in support vector classification

Advances in kernel methods
Making large-scale support vector machine learning practical

Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
Optimization Software Guide

Optimization Software Guide
Support Vector Machines: Training and Applications

Support Vector Machines: Training and Applications

A comparison of different initialization strategies to reduce the training time of support vector machines

ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a study of the Quadratic optimization Problem (QP) lying on the learning process of Support Vector Machines (SVM). Taking the Karush-Kuhn-Tucker (KKT) optimality conditions, we present the strategy of implementation of the SVM-QP following two classical approaches: i) active set, also divided in primal and dual spaces, methods and ii) interior point methods. We also present the general extension to treat large scale applications consisting in a general decomposition of the QP problem into smaller ones. In the same manner, we discuss some considerations to take into account to start the general learning process. We compare the performances of the optimization strategies using some well-known benchmark databases.