New Support Vector Algorithms
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A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case.Abstract:
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of ν, and report experimental results.read more
Citations
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Support Vector Machine for Classification Based on Fuzzy Training Data
TL;DR: This paper introduces the support vector machine in which the training examples are fuzzy input, and gives some solving procedure of the support vectors machine with fuzzy training data.
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Classification of diesel pool refinery streams through near infrared spectroscopy and support vector machines using C-SVC and ν-SVC
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Reduced Convex Hulls: A Geometric Approach to Support Vector Machines
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