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An introduction to support Vector Machines: and other kernel-based learning methods
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The theory of evolution strategies
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Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
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Radius margin bounds for support vector machines with the RBF kernel
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Speeding up backpropagation using multiobjective evolutionary algorithms
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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
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In this article, model selection for support vector machines is viewed as a multi-objective optimization problem, where model complexity and training accuracy define two conflicting objectives. Different optimization criteria are evaluated: Split modified radius margin bounds, which allow for comparing existing model selection criteria, and the training error in conjunction with the number of support vectors for designing sparse solutions.