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COLT
1999
Springer
15 years 11 months ago
Covering Numbers for Support Vector Machines
—Support vector (SV) machines are linear classifiers that use the maximum margin hyperplane in a feature space defined by a kernel function. Until recently, the only bounds on th...
Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Ro...
AAAI
2007
15 years 9 months ago
Unscented Message Passing for Arbitrary Continuous Variables in Bayesian Networks
Since Bayesian network (BN) was introduced in the field of artificial intelligence in 1980s, a number of inference algorithms have been developed for probabilistic reasoning. Ho...
Wei Sun, Kuo-Chu Chang
IPCO
2007
126views Optimization» more  IPCO 2007»
15 years 8 months ago
The Smoothed Number of Pareto Optimal Solutions in Bicriteria Integer Optimization
Abstract. A well established heuristic approach for solving various bicriteria optimization problems is to enumerate the set of Pareto optimal solutions, typically using some kind ...
René Beier, Heiko Röglin, Berthold V&o...
NIPS
2004
15 years 8 months ago
Parametric Embedding for Class Visualization
In this paper, we propose a new method, Parametric Embedding (PE), for visualizing the posteriors estimated over a mixture model. PE simultaneously embeds both objects and their c...
Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean S...
NIPS
2000
15 years 8 months ago
A Support Vector Method for Clustering
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to d...
Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladi...