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» On the Complexity of Function Learning
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JMLR
2002
74views more  JMLR 2002»
15 years 4 months ago
The Representational Power of Discrete Bayesian Networks
One of the most important fundamental properties of Bayesian networks is the representational power, reflecting what kind of functions they can or cannot represent. In this paper,...
Charles X. Ling, Huajie Zhang
134
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SIGIR
2006
ACM
15 years 10 months ago
Learning to advertise
Content-targeted advertising, the task of automatically associating ads to a Web page, constitutes a key Web monetization strategy nowadays. Further, it introduces new challenging...
Anísio Lacerda, Marco Cristo, Marcos Andr&e...
AAAI
2007
15 years 7 months ago
Semi-Supervised Learning by Mixed Label Propagation
Recent studies have shown that graph-based approaches are effective for semi-supervised learning. The key idea behind many graph-based approaches is to enforce the consistency bet...
Wei Tong, Rong Jin
NIPS
2001
15 years 6 months ago
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
Policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation (Baxter and Bartlett, 2001...
Evan Greensmith, Peter L. Bartlett, Jonathan Baxte...
NN
2000
Springer
192views Neural Networks» more  NN 2000»
15 years 4 months ago
A new algorithm for learning in piecewise-linear neural networks
Piecewise-linear (PWL) neural networks are widely known for their amenability to digital implementation. This paper presents a new algorithm for learning in PWL networks consistin...
Emad Gad, Amir F. Atiya, Samir I. Shaheen, Ayman E...