A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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Support Vector Machines (SVMs) are learning machines that can perform binary classification (pattern recognition) and real valued function approximation (regression estimation) tasks. An inverse problem of SVMs is how to split a given dataset into two clusters such that the maximum margin between the two clusters is attained. Here the margin is defined according to the separating hyper-plane generated by support vectors. This paper investigates the inverse problem of SVMs by designing a parallel genetic algorithm. Experiments show that this algorithm can greatly decrease time complexity by the use of parallel processing. This study on the inverse problem of SVMs is motivated by designing a heuristic algorithm for generating decision trees with high generalization capability.