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ICML
2010
IEEE
15 years 5 months ago
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable t...
Mingkui Tan, Li Wang, Ivor W. Tsang
130
Voted
ECML
2005
Springer
15 years 10 months ago
Model-Based Online Learning of POMDPs
Abstract. Learning to act in an unknown partially observable domain is a difficult variant of the reinforcement learning paradigm. Research in the area has focused on model-free m...
Guy Shani, Ronen I. Brafman, Solomon Eyal Shimony
WSDM
2012
ACM
259views Data Mining» more  WSDM 2012»
14 years 3 days ago
Learning recommender systems with adaptive regularization
Many factorization models like matrix or tensor factorization have been proposed for the important application of recommender systems. The success of such factorization models dep...
Steffen Rendle
SODA
2001
ACM
147views Algorithms» more  SODA 2001»
15 years 6 months ago
Sublinear time approximate clustering
Clustering is of central importance in a number of disciplines including Machine Learning, Statistics, and Data Mining. This paper has two foci: 1 It describes how existing algori...
Nina Mishra, Daniel Oblinger, Leonard Pitt
181
Voted
CORR
2012
Springer
170views Education» more  CORR 2012»
14 years 8 days ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson