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» Dynamic power management using machine learning
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NECO
2008
112views more  NECO 2008»
15 years 3 months ago
Second-Order SMO Improves SVM Online and Active Learning
Iterative learning algorithms that approximate the solution of support vector machines (SVMs) have two potential advantages. First, they allow for online and active learning. Seco...
Tobias Glasmachers, Christian Igel
ESWA
2007
127views more  ESWA 2007»
15 years 3 months ago
Clustering support vector machines for protein local structure prediction
Understanding the sequence-to-structure relationship is a central task in bioinformatics research. Adequate knowledge about this relationship can potentially improve accuracy for ...
Wei Zhong, Jieyue He, Robert W. Harrison, Phang C....
NIPS
2008
15 years 4 months ago
Analyzing human feature learning as nonparametric Bayesian inference
Almost all successful machine learning algorithms and cognitive models require powerful representations capturing the features that are relevant to a particular problem. We draw o...
Joseph Austerweil, Thomas L. Griffiths
160
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ML
2008
ACM
152views Machine Learning» more  ML 2008»
15 years 3 months ago
Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path
Abstract. We consider batch reinforcement learning problems in continuous space, expected total discounted-reward Markovian Decision Problems. As opposed to previous theoretical wo...
András Antos, Csaba Szepesvári, R&ea...
128
Voted
IPPS
2007
IEEE
15 years 9 months ago
Scaling and Packing on a Chip Multiprocessor
Power management is critical in server and high-performancecomputing environments as well as in mobile computing. Many mechanisms have been developed over recent years to support ...
Vincent W. Freeh, Tyler K. Bletsch, Freeman L. Raw...