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)
Machine learning approach for IP-flow record anomaly detection
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part I
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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 better performance than conventional learning methods in many applications. This paper proposes a weighted kernel function for support vector machine and its learning method with a fast convergence and a good classification performance. We defined the weighted 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 learning method based on genetic algorithm. The weights of basis kernel functions in proposed kernel are determined in learning phase and used as the parameters in the decision model in classification phase. The experiments on several clinical datasets such as colon cancer, leukemia cancer, and lung cancer datasets indicate that our weighted kernel function results in higher and more stable classification performance than other kernel functions. Our method also has comparable and sometimes better classification performance than other classification methods for certain applications.