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ICML
2008
IEEE
16 years 7 months ago
Discriminative parameter learning for Bayesian networks
Bayesian network classifiers have been widely used for classification problems. Given a fixed Bayesian network structure, parameters learning can take two different approaches: ge...
Jiang Su, Harry Zhang, Charles X. Ling, Stan Matwi...
ICML
2006
IEEE
16 years 7 months ago
A continuation method for semi-supervised SVMs
Semi-Supervised Support Vector Machines (S3 VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do n...
Olivier Chapelle, Mingmin Chi, Alexander Zien
ICML
2006
IEEE
16 years 7 months ago
Inference with the Universum
In this paper we study a new framework introduced by Vapnik (1998) and Vapnik (2006) that is an alternative capacity concept to the large margin approach. In the particular case o...
Fabian H. Sinz, Jason Weston, Léon Bottou, ...
ICML
2005
IEEE
16 years 7 months ago
Predictive low-rank decomposition for kernel methods
Low-rank matrix decompositions are essential tools in the application of kernel methods to large-scale learning problems. These decompositions have generally been treated as black...
Francis R. Bach, Michael I. Jordan
ICML
2005
IEEE
16 years 7 months ago
Modeling word burstiness using the Dirichlet distribution
Multinomial distributions are often used to model text documents. However, they do not capture well the phenomenon that words in a document tend to appear in bursts: if a word app...
Rasmus Elsborg Madsen, David Kauchak, Charles Elka...