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» Unsupervised Greedy Learning of Finite Mixture Models
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IJON
2006
99views more  IJON 2006»
13 years 7 months ago
Learning vector quantization: The dynamics of winner-takes-all algorithms
Winner-Takes-All (WTA) prescriptions for Learning Vector Quantization (LVQ) are studied in the framework of a model situation: Two competing prototype vectors are updated accordin...
Michael Biehl, Anarta Ghosh, Barbara Hammer
JAIR
1998
198views more  JAIR 1998»
13 years 7 months ago
Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Alberto Ruiz, Pedro E. López-de-Teruel, M. ...
NIPS
2008
13 years 9 months ago
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification
Probabilistic topic models have become popular as methods for dimensionality reduction in collections of text documents or images. These models are usually treated as generative m...
Simon Lacoste-Julien, Fei Sha, Michael I. Jordan
SADM
2010
141views more  SADM 2010»
13 years 2 months ago
A parametric mixture model for clustering multivariate binary data
: The traditional latent class analysis (LCA) uses a mixture model with binary responses on each subject that are independent conditional on cluster membership. However, in many pr...
Ajit C. Tamhane, Dingxi Qiu, Bruce E. Ankenman
AND
2009
13 years 5 months ago
Discovering voter preferences in blogs using mixtures of topic models
In this paper we propose a new approach to capture the inclination towards a certain election candidate from the contents of blogs and to explain why that inclination may be so. T...
Pradipto Das, Rohini K. Srihari, Smruthi Mukund