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ICML
2003
IEEE
14 years 10 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...
ICONIP
2007
13 years 11 months ago
Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes
Abstract. An effective way to examine causality is to conduct an experiment with random assignment. However, in many cases it is impossible or too expensive to perform controlled ...
Shohei Shimizu, Aapo Hyvärinen
TSMC
2010
13 years 4 months ago
Dual Gait Generative Models for Human Motion Estimation From a Single Camera
This paper presents a general gait representation framework for video-based human motion estimation. Specifically, we want to estimate the kinematics of an unknown gait from image ...
Xin Zhang, Guoliang Fan
AAAI
2006
13 years 11 months ago
Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification
This paper presents multi-conditional learning (MCL), a training criterion based on a product of multiple conditional likelihoods. When combining the traditional conditional proba...
Andrew McCallum, Chris Pal, Gregory Druck, Xuerui ...
ICDM
2005
IEEE
137views Data Mining» more  ICDM 2005»
14 years 3 months ago
Leveraging Relational Autocorrelation with Latent Group Models
The presence of autocorrelation provides a strong motivation for using relational learning and inference techniques. Autocorrelation is a statistical dependence between the values...
Jennifer Neville, David Jensen