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FOCS
1990
IEEE
15 years 11 months ago
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain
Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the ...
Avrim Blum
EENERGY
2010
15 years 10 months ago
Towards energy-aware scheduling in data centers using machine learning
As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to find better an...
Josep Lluis Berral, Iñigo Goiri, Ramon Nou,...
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
15 years 10 months ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa
FLAIRS
2008
15 years 9 months ago
Learning in the Lexical-Grammatical Interface
Children are facile at both discovering word boundaries and using those words to build higher-level structures in tandem. Current research treats lexical acquisition and grammar i...
Tom Armstrong, Tim Oates
CORR
2010
Springer
109views Education» more  CORR 2010»
15 years 7 months ago
Polynomial Learning of Distribution Families
Abstract--The question of polynomial learnability of probability distributions, particularly Gaussian mixture distributions, has recently received significant attention in theoreti...
Mikhail Belkin, Kaushik Sinha