A hybrid random field model for scalable statistical learning-Reference-Cited by-同舟云学术

A hybrid random field model for scalable statistical learning

Published:2009-07 Issue:5-6 Volume:22 Page:603-613
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Freno A.,Trentin E.,Gori M.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference40 articles.

1. Spatial interaction and the statistical analysis of lattice systems;Besag;Journal of the Royal Statistical Society. Series B,1974

2. Statistical analysis of non-lattice data;Besag;The Statistician,1975

3. Breese, J. S., Heckerman, D., & Kadie, C. M. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the fourteenth annual conference on uncertainty in artificial intelligence (pp. 43–52)

4. Theory refinement on Bayesian networks;Buntine,1991

5. Operations for learning with graphical models;Buntine;Journal of Artificial Intelligence Research,1994

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