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CIARP
2010
Springer
13 years 5 months ago
A New Algorithm for Training SVMs Using Approximate Minimal Enclosing Balls
Abstract. It has been shown that many kernel methods can be equivalently formulated as minimal-enclosing-ball (MEB) problems in certain feature space. Exploiting this reduction eff...
Emanuele Frandi, Maria Grazia Gasparo, Stefano Lod...
COLT
2005
Springer
14 years 1 months ago
Learning Convex Combinations of Continuously Parameterized Basic Kernels
We study the problem of learning a kernel which minimizes a regularization error functional such as that used in regularization networks or support vector machines. We consider thi...
Andreas Argyriou, Charles A. Micchelli, Massimilia...
SDM
2010
SIAM
151views Data Mining» more  SDM 2010»
13 years 9 months ago
Fast Stochastic Frank-Wolfe Algorithms for Nonlinear SVMs
The high computational cost of nonlinear support vector machines has limited their usability for large-scale problems. We propose two novel stochastic algorithms to tackle this pr...
Hua Ouyang, Alexander Gray
ECIR
2010
Springer
13 years 5 months ago
Maximum Margin Ranking Algorithms for Information Retrieval
Abstract. Machine learning ranking methods are increasingly applied to ranking tasks in information retrieval (IR). However ranking tasks in IR often differ from standard ranking t...
Shivani Agarwal, Michael Collins
NIPS
2007
13 years 9 months ago
Bundle Methods for Machine Learning
We present a globally convergent method for regularized risk minimization problems. Our method applies to Support Vector estimation, regression, Gaussian Processes, and any other ...
Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le