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» Dynamic power management using machine learning
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154
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ICML
1999
IEEE
16 years 4 months ago
AdaCost: Misclassification Cost-Sensitive Boosting
AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boo...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip...
ECML
2006
Springer
15 years 7 months ago
TildeCRF: Conditional Random Fields for Logical Sequences
Abstract. Conditional Random Fields (CRFs) provide a powerful instrument for labeling sequences. So far, however, CRFs have only been considered for labeling sequences over flat al...
Bernd Gutmann, Kristian Kersting
NIPS
2007
15 years 5 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 ...
137
Voted
ICML
2010
IEEE
15 years 4 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
SPIRE
1999
Springer
15 years 7 months ago
Using AulaNet for Web-based Course Development
: A variety of technologies is being used to replace or supplement the face-to-face learning process, including the World Wide Web. In this paper we present AulaNet, an environment...
Ricardo Choren, Carlos Laufer, Marcelo Blois, Vivi...