We introduce a family of positive definite kernels specifically designed for problems described by categorical information. The kernels are based on the comparison of the probability mass function of the variables and have a clear interpretation in terms of similarity computations between the modalities.
We report experimental results on two different problems in the life sciences indicating that the proposed approach may markedly outperform standard kernels, so it can be used as a good alternative to other common kernel functions (at least for SVM classification) in order to obtain better accuracy.
CitationBelanche, Ll.; Villegas, M. Kernel functions for categorical variables with application to problems in the life sciences. A: Congrés Internacional de l’Associació Catalana d’Intel·ligència Artificial. "Artificial intelligence research and development : proceedings of the 16 International Conference of the Catalan Association of Artificial Intelligence". Vic: 2013, p. 171-180.
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder. If you wish to make any use of the work not provided for in the law, please contact: firstname.lastname@example.org