Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
An introduction to genetic algorithms
An introduction to genetic algorithms
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Efficient SVM Regression Training with SMO
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Evolutionary tuning of multiple SVM parameters
Neurocomputing
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Data Mining of Agricultural Yield Data: A Comparison of Regression Models
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Regression models for spatial data: an example from precision agriculture
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Using genetic algorithms to improve prediction of execution times of ML tasks
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
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A genetic approach is presented in this article to deal with two problems: a) feature selection and b) the determination of parameters in Support Vector Regression (SVR). We consider a kind of genetic algorithm (GA) in which the probabilities of mutation and crossover are determined in the evolutionary process. Some empirical experiments are made to measure the efficiency of this algorithm against two frequently used approaches.