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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Williamson, estimating the support of a high-dimensional distribution
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Estimating the Support of a High-Dimensional Distribution
TL;DR: In this paper, the authors propose a method to estimate a function f that is positive on S and negative on the complement of S. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space.
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No-Reference Image Quality Assessment in the Spatial Domain
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An introduction to kernel-based learning algorithms
TL;DR: This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods.
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