Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
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)
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This paper proposes a unified kernel function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the unified kernel function as the weighted sum of a set of different types of basis kernel functions such as neural, radial, and polynomial kernels, which are trained by a new learning method based on genetic algorithm. The weights of basis kernel functions in the unified kernel are determined in learning phase and used as the parameters in the decision model in the classification phase. The unified kernel and the learning method were applied to obtain the optimal decision model for the classification of two public data sets for diagnosis of cancer diseases. The experiment showed fast convergence in learning phase and resulted in the optimal decision model with the better performance than other kernels. Therefore, the proposed kernel function has the greater flexibility in representing a problem space than other kernel functions.