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GECCO
2007
Springer
144views Optimization» more  GECCO 2007»
15 years 1 months ago
Mixing independent classifiers
In this study we deal with the mixing problem, which concerns combining the prediction of independently trained local models to form a global prediction. We deal with it from the ...
Jan Drugowitsch, Alwyn Barry
IFIP
2010
Springer
14 years 4 months ago
Combining Software and Hardware LCS for Lightweight On-Chip Learning
In this paper we present a novel two-stage method to realize a lightweight but very capable hardware implementation of a Learning Classifier System for on-chip learning. Learning C...
Andreas Bernauer, Johannes Zeppenfeld, Oliver Brin...
JMLR
2012
13 years 4 days ago
Beyond Logarithmic Bounds in Online Learning
We prove logarithmic regret bounds that depend on the loss L∗ T of the competitor rather than on the number T of time steps. In the general online convex optimization setting, o...
Francesco Orabona, Nicolò Cesa-Bianchi, Cla...
ML
2002
ACM
121views Machine Learning» more  ML 2002»
14 years 9 months ago
Near-Optimal Reinforcement Learning in Polynomial Time
We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decisio...
Michael J. Kearns, Satinder P. Singh
ALT
2010
Springer
14 years 11 months ago
A Lower Bound for Learning Distributions Generated by Probabilistic Automata
Known algorithms for learning PDFA can only be shown to run in time polynomial in the so-called distinguishability
Borja Balle, Jorge Castro, Ricard Gavaldà