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
Convex Optimization
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Neural Computation
Efficient Computation and Model Selection for the Support Vector Regression
Neural Computation
A kernel path algorithm for support vector machines
Proceedings of the 24th international conference on Machine learning
A New Solution Path Algorithm in Support Vector Regression
IEEE Transactions on Neural Networks
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The ν-Support Vector Classification (ν-SVC) proposed by Schölkopf et al. has the advantage of using a regularization parameter ν on controlling the number of support vectors and margin errors. However, comparing to C-SVC, its formulation is more complicated, up to now there are no effective methods on computing the regularization path for it. In this paper, we propose a new regularization path algorithm, which is designed based on a modified formulation of ν-SVC and traces the solution path with respect to the parameter ν.