Harmony Search applied for Support Vector Machines Training Optimization
Nenhuma Miniatura disponível
Data
2013-01-01
Autores
Pereira, Luis A. M. [UNESP]
Papa, João Paulo [UNESP]
Souza, Andre N. de
Título da Revista
ISSN da Revista
Título de Volume
Editor
Ieee
Resumo
Since the beginning, some pattern recognition techniques have faced the problem of high computational burden for dataset learning. Among the most widely used techniques, we may highlight Support Vector Machines (SVM), which have obtained very promising results for data classification. However, this classifier requires an expensive training phase, which is dominated by a parameter optimization that aims to make SVM less prone to errors over the training set. In this paper, we model the problem of finding such parameters as a metaheuristic-based optimization task, which is performed through Harmony Search (HS) and some of its variants. The experimental results have showen the robustness of HS-based approaches for such task in comparison against with an exhaustive (grid) search, and also a Particle Swarm Optimization-based implementation.
Descrição
Palavras-chave
Support Vector Machines, Harmony Search, Fault Detections
Como citar
2013 Ieee Eurocon. New York: Ieee, p. 998-1002, 2013.