An effective regularization path for ν-support vector classification
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
An improved algorithm for the solution of the regularization path of support vector machine
IEEE Transactions on Neural Networks
A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling
IEEE Transactions on Fuzzy Systems
Multiple incremental decremental learning of support vector machines
IEEE Transactions on Neural Networks
Regression transfer learning based on principal curve
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Model combination for support vector regression via regularization path
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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In this paper, regularization path algorithms were proposed as a novel approach to the model selection problem by exploring the path of possibly all solutions with respect to some regularization hyperparameter in an efficient way. This approach was later extended to a support vector regression (SVR) model called epsiv -SVR. However, the method requires that the error parameter epsiv be set a priori. This is only possible if the desired accuracy of the approximation can be specified in advance. In this paper, we analyze the solution space for epsiv-SVR and propose a new solution path algorithm, called epsiv-path algorithm, which traces the solution path with respect to the hyperparameter epsiv rather than lambda. Although both two solution path algorithms possess the desirable piecewise linearity property, our epsiv-path algorithm overcomes some limitations of the original lambda-path algorithm and has more advantages. It is thus more appealing for practical use.