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TIT
1998
123views more  TIT 1998»
15 years 4 months ago
The Minimum Description Length Principle in Coding and Modeling
—We review the principles of Minimum Description Length and Stochastic Complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the...
Andrew R. Barron, Jorma Rissanen, Bin Yu
IJON
2010
109views more  IJON 2010»
14 years 11 months ago
Variational inference for Student-t MLP models
This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for ...
Hang T. Nguyen, Ian T. Nabney
169
Voted
JMLR
2010
132views more  JMLR 2010»
14 years 11 months ago
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean...
Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mu...
JMLR
2010
150views more  JMLR 2010»
14 years 11 months ago
Supervised Dimension Reduction Using Bayesian Mixture Modeling
We develop a Bayesian framework for supervised dimension reduction using a flexible nonparametric Bayesian mixture modeling approach. Our method retrieves the dimension reduction ...
Kai Mao, Feng Liang, Sayan Mukherjee
FGR
2011
IEEE
198views Biometrics» more  FGR 2011»
14 years 8 months ago
The optimal camera arrangement by a performance model for gait recognition
— Recently, many gait recognition algorithms are proposed, and the optimal camera arrangement is necessary to maximize the performance. In this paper, we propose the optimal came...
Naoki Akae, Yasushi Makihara, Yasushi Yagi