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.
['Qiang He', 'Xi-Zhao Wang', 'Jun-Fen Chen', 'Leifan Yan']
A parallel genetic algorithm for solving the inverse problem of support vector machines