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» An Instance Selection Approach to Multiple Instance Learning
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AAAI
2012
13 years 4 days ago
Multi-Label Learning by Exploiting Label Correlations Locally
It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically exploit label correlations globally, by assuming that the ...
Sheng-Jun Huang, Zhi-Hua Zhou
ECML
2005
Springer
15 years 3 months ago
Active Learning in Partially Observable Markov Decision Processes
This paper examines the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not known or is only poorly specified. W...
Robin Jaulmes, Joelle Pineau, Doina Precup
AUTONOMICS
2007
ACM
15 years 1 months ago
A framework to support multiple reconfiguration strategies
Self-management is a key feature of autonomic systems. This often demands the dynamic reconfiguration of a distributed application. An important issue in the reconfiguration proce...
Liliana Rosa, Luís Rodrigues, Antóni...
JMLR
2010
126views more  JMLR 2010»
14 years 4 months ago
Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach
We propose a novel application of the Simultaneous Orthogonal Matching Pursuit (SOMP) procedure to perform variable selection in ultra-high dimensional multiple output regression ...
Mladen Kolar, Eric P. Xing
LION
2010
Springer
190views Optimization» more  LION 2010»
15 years 1 months ago
Algorithm Selection as a Bandit Problem with Unbounded Losses
Abstract. Algorithm selection is typically based on models of algorithm performance learned during a separate offline training sequence, which can be prohibitively expensive. In r...
Matteo Gagliolo, Jürgen Schmidhuber