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NIPS
2007
14 years 11 months ago
A Risk Minimization Principle for a Class of Parzen Estimators
This paper1 explores the use of a Maximal Average Margin (MAM) optimality principle for the design of learning algorithms. It is shown that the application of this risk minimizati...
Kristiaan Pelckmans, Johan A. K. Suykens, Bart De ...
96
Voted
IEEEPACT
2008
IEEE
15 years 3 months ago
Feature selection and policy optimization for distributed instruction placement using reinforcement learning
Communication overheads are one of the fundamental challenges in a multiprocessor system. As the number of processors on a chip increases, communication overheads and the distribu...
Katherine E. Coons, Behnam Robatmili, Matthew E. T...
GECCO
2007
Springer
173views Optimization» more  GECCO 2007»
15 years 3 months ago
UCSpv: principled voting in UCS rule populations
Credit assignment is a fundamental issue for the Learning Classifier Systems literature. We engage in a detailed investigation of credit assignment in one recent system called UC...
Gavin Brown, Tim Kovacs, James A. R. Marshall
112
Voted
JMLR
2012
12 years 12 months ago
Multi Kernel Learning with Online-Batch Optimization
In recent years there has been a lot of interest in designing principled classification algorithms over multiple cues, based on the intuitive notion that using more features shou...
Francesco Orabona, Jie Luo, Barbara Caputo
ICML
2010
IEEE
14 years 10 months ago
Modeling Interaction via the Principle of Maximum Causal Entropy
The principle of maximum entropy provides a powerful framework for statistical models of joint, conditional, and marginal distributions. However, there are many important distribu...
Brian Ziebart, J. Andrew Bagnell, Anind K. Dey