495 search results - page 46 / 99 » Approximation algorithms for budgeted learning problems |
101
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IR
2010
14 years 8 months ago
2010
Recently direct optimization of information retrieval (IR) measures becomes a new trend in learning to rank. Several methods have been proposed and the effectiveness of them has ...
139
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PKDD
2010
Springer
14 years 7 months ago
2010
Springer
Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
AAAI
2006
14 years 11 months ago
2006
L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized...
ICML
2005
IEEE
15 years 10 months ago
2005
IEEE
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...
CVPR
2007
IEEE
15 years 11 months ago
2007
IEEE
The success of tensor-based subspace learning depends heavily on reducing correlations along the column vectors of the mode-k flattened matrix. In this work, we study the problem ...