KMOD - A New Support Vector Machine Kernel with Moderate Decreasing for Pattern Recognition. Application to Digit Image Recognition

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Venue:
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Year:
2001

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Abstract

Abstract: A new direction in machine learning area has emerged from Vapnik's theory in support vectors machine and its applications on pattern recognition. In this paper, we propose a new SVM kernel family (KMOD) with distinctive properties that allow better discrimination in the feature space. The experiments that we carry out show its effectiveness on synthetic and large-scale data. We found KMOD behaving better than RBF and Exponential RBF kernels on the two-spiral problem. In addition, a digit recognition task was processed using the proposed kernel. The results show, at least, comparable performances to state of the art kernels.