Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A study of crossover operators for gene selection of microarray data
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
DRFE: dynamic recursive feature elimination for gene identification based on random forest
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Feature elimination approach based on random forest for cancer diagnosis
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Gene expression profiling using flexible neural trees
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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The problem of determining optimal decision model is a difficult combinatorial task in the fields of pattern classification, machine learning, and especially bioinformatics. Recently, support vector machine (SVM) has shown a higher performance than conventional learning methods in many applications. This paper proposes a new kernel function for support vector machine (SVM) and its learning method that results in fast convergence and good classification performance. The new kernel function is created by combining a set of kernel functions. A new learning method based on evolution algorithm (EA) is proposed to obtain the optimal decision model consisting of an optimal set of features as well as an optimal set of the parameters for combined kernel function. The experiments on clinical datasets such as stomach cancer, colon cancer, and leukemia datasets data sets indicates that the combined kernel function shows higher and more stable classification performance than other kernel functions.