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JMLR
2006
89views more  JMLR 2006»
15 years 6 months ago
Maximum-Gain Working Set Selection for SVMs
Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small wor...
Tobias Glasmachers, Christian Igel
JMLR
2006
143views more  JMLR 2006»
15 years 6 months ago
Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
We determine the asymptotic behaviour of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function i...
Régis Vert, Jean-Philippe Vert
JMLR
2006
116views more  JMLR 2006»
15 years 6 months ago
Step Size Adaptation in Reproducing Kernel Hilbert Space
This paper presents an online support vector machine (SVM) that uses the stochastic meta-descent (SMD) algorithm to adapt its step size automatically. We formulate the online lear...
S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex ...
MP
2006
137views more  MP 2006»
15 years 6 months ago
New algorithms for singly linearly constrained quadratic programs subject to lower and upper bounds
There are many applications related to singly linearly constrained quadratic programs subjected to upper and lower bounds. In this paper, a new algorithm based on secant approximat...
Yu-Hong Dai, Roger Fletcher
PR
2008
104views more  PR 2008»
15 years 6 months ago
Generative models for similarity-based classification
A maximum-entropy approach to generative similarity-based classifiers model is proposed. First, a descriptive set of similarity statistics is assumed to be sufficient for classifi...
Luca Cazzanti, Maya R. Gupta, Anjali J. Koppal