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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
ICML
2004
IEEE
15 years 10 months ago
Learning first-order rules from data with multiple parts: applications on mining chemical compound data
Inductive learning of first-order theory based on examples has serious bottleneck in the enormous hypothesis search space needed, making existing learning approaches perform poorl...
Cholwich Nattee, Sukree Sinthupinyo, Masayuki Numa...
GECCO
2009
Springer
188views Optimization» more  GECCO 2009»
15 years 1 months ago
Exploiting multiple classifier types with active learning
Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...
Zhenyu Lu, Josh Bongard
VLDB
2001
ACM
190views Database» more  VLDB 2001»
15 years 2 months ago
LEO - DB2's LEarning Optimizer
Most modern DBMS optimizers rely upon a cost model to choose the best query execution plan (QEP) for any given query. Cost estimates are heavily dependent upon the optimizer’s e...
Michael Stillger, Guy M. Lohman, Volker Markl, Mok...
ICASSP
2011
IEEE
14 years 1 months ago
Multiple instance tracking based on hierarchical maximizing bag's margin boosting
In online tracking, the tracker evolves to reflect variations in object appearance and surroundings. This updating process is formulated as a supervised learning problem, thus a ...
Chunxiao Liu, Guijin Wang, Xinggang Lin, Bobo Zeng