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KDD
2004
ACM
158views Data Mining» more  KDD 2004»
14 years 4 months ago
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Co-clustering is a powerful data mining technique with varied applications such as text clustering, microarray analysis and recommender systems. Recently, an informationtheoretic ...
Arindam Banerjee, Inderjit S. Dhillon, Joydeep Gho...
WWW
2007
ACM
14 years 5 months ago
Modeling user behavior in recommender systems based on maximum entropy
We propose a model for user purchase behavior in online stores that provide recommendation services. We model the purchase probability given recommendations for each user based on...
Tomoharu Iwata, Kazumi Saito, Takeshi Yamada
ICML
2003
IEEE
14 years 5 months ago
Learning Mixture Models with the Latent Maximum Entropy Principle
We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from ...
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin...
ICPR
2004
IEEE
14 years 5 months ago
To FRAME or not to FRAME in Probabilistic Texture Modelling?
The maximum entropy principle is a cornerstone of FRAME (Filters, RAndom fields, and Maximum Entropy) model considered at times as a first-ever step towards a universal theory of ...
Georgy L. Gimel'farb, Luc J. Van Gool, Alexey Zale...
ICCV
2001
IEEE
14 years 6 months ago
Learning Inhomogeneous Gibbs Model of Faces by Minimax Entropy
In this paper we propose a novel inhomogeneous Gibbs model by the minimax entropy principle, and apply it to face modeling. The maximum entropy principle generalizes the statistic...
Ce Liu, Song Chun Zhu, Heung-Yeung Shum