Feature selection based on genetic algorithms and support vector machines for handwritten similar Chinese characters recognition

2005 ◽  
Author(s):  
Jun Feng ◽  
Yang Yang ◽  
Hong Wang ◽  
Xian-Mei Wang
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Support Vector Machines ◽  
Support Vector ◽  
Chinese Characters ◽  
Vector Machines

FEATURE SELECTION FOR SUPPORT VECTOR MACHINES USING GENETIC ALGORITHMS

2004 ◽  
Vol 13 (04) ◽  
pp. 791-800 ◽  
Author(s):  
HOLGER FRÖHLICH ◽  
OLIVIER CHAPELLE ◽  
BERNHARD SCHÖLKOPF
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Support Vector Machines ◽  
Cross Validation ◽  
Support Vector ◽  
Generalization Error ◽  
New Approach ◽  
Vector Machines ◽  
Selection For ◽  
Natural Way

The problem of feature selection is a difficult combinatorial task in Machine Learning and of high practical relevance, e.g. in bioinformatics. Genetic Algorithms (GAs) offer a natural way to solve this problem. In this paper we present a special Genetic Algorithm, which especially takes into account the existing bounds on the generalization error for Support Vector Machines (SVMs). This new approach is compared to the traditional method of performing cross-validation and to other existing algorithms for feature selection.

Support vector machines and genetic algorithms for detecting unstable angina

2003 ◽  
Author(s):  
J. Sepulveda-Sanchis ◽  
G. Camps-Valls ◽  
E. Soria-Olivas ◽  
S. Salcedo-Sanz ◽  
C. Bousono-Calzon ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Support Vector Machines ◽  
Unstable Angina ◽  
Support Vector ◽  
Vector Machines

scholarly journals Inspeção Automática de Defeitos em Madeiras de Pinus usando Visão Computacional

2008 ◽  
Vol 15 (2) ◽  
pp. 203-218
Author(s):  
Luiz E. S. Oliveira ◽  
Paulo R. Cavalin ◽  
Alceu S. Britto Jr ◽  
Alessandro L. Koerich
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Defect Detection ◽  
Color Image ◽  
Support Vector ◽  
Grayscale Image ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Vector Machines

This paper addresses the issue of detecting defects in Pine wood using features extracted from grayscale images. The feature set proposed here is based on the concept of texture and it is computed from the co-occurrence matrices. The features provide measures of properties such as smoothness, coarseness, and regularity. Comparative experiments using a color image based feature set extracted from percentile histograms are carried to demonstrate the efficiency of the proposed feature set. Two different learning paradigms, neural networks and support vector machines, and a feature selection algorithm based on multi-objective genetic algorithms were considered in our experiments. The experimental results show that after feature selection, the grayscale image based feature set achieves very competitive performance for the problem of wood defect detection relative to the color image based features.

Sign in / Sign up

Export Citation Format

Share Document