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» A comparison of empirical and model-driven optimization
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ACML
2009
Springer
15 years 3 months ago
Max-margin Multiple-Instance Learning via Semidefinite Programming
In this paper, we present a novel semidefinite programming approach for multiple-instance learning. We first formulate the multipleinstance learning as a combinatorial maximum marg...
Yuhong Guo
GECCO
2006
Springer
157views Optimization» more  GECCO 2006»
15 years 3 months ago
gLINC: identifying composability using group perturbation
We present two novel perturbation-based linkage learning algorithms that extend LINC [5]; a version of LINC optimised for decomposition tasks (oLINC) and a hierarchical version of...
David Jonathan Coffin, Christopher D. Clack
AIPS
2009
15 years 23 days ago
Suboptimal and Anytime Heuristic Search on Multi-Core Machines
In order to scale with modern processors, planning algorithms must become multi-threaded. In this paper, we present parallel shared-memory algorithms for two problems that underli...
Ethan Burns, Seth Lemons, Wheeler Ruml, Rong Zhou
ICML
2010
IEEE
15 years 22 days ago
Efficient Selection of Multiple Bandit Arms: Theory and Practice
We consider the general, widely applicable problem of selecting from n real-valued random variables a subset of size m of those with the highest means, based on as few samples as ...
Shivaram Kalyanakrishnan, Peter Stone
CORR
2000
Springer
84views Education» more  CORR 2000»
14 years 11 months ago
Robust Classification for Imprecise Environments
In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building ro...
Foster J. Provost, Tom Fawcett