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ICML
2009
IEEE
16 years 7 months ago
Learning when to stop thinking and do something!
An anytime algorithm is capable of returning a response to the given task at essentially any time; typically the quality of the response improves as the time increases. Here, we c...
Barnabás Póczos, Csaba Szepesv&aacut...
ECML
2007
Springer
15 years 10 months ago
Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass
NIPS
2003
15 years 8 months ago
Online Learning via Global Feedback for Phrase Recognition
We present a system to recognize phrases based on perceptrons, and a global online learning algorithm to train them together. The recognition strategy applies learning in two laye...
Xavier Carreras, Lluís Màrquez
ICML
2006
IEEE
16 years 7 months ago
Full Bayesian network classifiers
The structure of a Bayesian network (BN) encodes variable independence. Learning the structure of a BN, however, is typically of high computational complexity. In this paper, we e...
Jiang Su, Harry Zhang
IPPS
2008
IEEE
16 years 1 months ago
Model-based fault localization in large-scale computing systems
We propose a new fault localization technique for software bugs in large-scale computing systems. Our technique always collects per-process function call traces of a target system...
Naoya Maruyama, Satoshi Matsuoka