The nature of statistical learning theory
The nature of statistical learning theory
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Evolutionary tuning of multiple SVM parameters
Neurocomputing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Statistical processes monitoring based on improved ICA and SVDD
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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The problem of kernel parameters selection for one-class classifier, ν-SVM, is studied. An improved constrained particle swarm optimization (PSO) is proposed to optimize the RBF kernel parameters of the ν-SVM and two kinds of flexible RBF kernels are introduced. As a general purpose swarm intelligent and global optimization tool, PSO do not need the classifier performance criterion to be differentiable and convex. In order to handle the parameter constraints involved by the ν-SVM, the improved constrained PSO utilizes the punishment term to provide the constraints violation information. Application studies on an artificial banana dataset the efficiency of the proposed method