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Tabu search model selection for SVM
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DOI：10.1142/S0129065708001348.png)
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Abstract
En 中文
A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. The aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together. The selection of the simplification level by vector quantization, of a feature subset and of support vector machines hyperparameters are performed by tabu search method to optimize the defined decision function quality criterion in order to find a good sub-optimal model on tractable times.
Keywords:
model selection
metaheuristic
tabu search
machine learning
Support Vector Machines
vector quantisation
pattern recognition
data mining
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