Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Kernels for Semi-Structured Data
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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With the growing interest of biological data prediction and chemical data prediction, more and more complicated kernels are designed to integrate data structures and relationships. We proposed a kind of evolutionary granular kernel trees (EGKTs) for drug activity comparisons [1]. In EGKTs, feature granules and tree structures are predefined based on the possible substituent locations. In this paper, we present a new system to evolve the structures of granular kernel trees (GKTs) in the case that we lack knowledge to predefine kernel trees. The new granular kernel tree structure evolving system is used for cyclooxygenase-2 inhibitor activity comparison. Experimental results show that the new system can achieve better performance than SVMs with traditional RBF kernels in terms of prediction accuracy.