Newton Method for the ICA Mixture Model

Source URL: dsp.ucsd.edu

Semi-Supervised Learning with the Deep Rendering Mixture Model Tan Nguyen1,2 Wanjia Liu1 Ethan Perez1 Richard G. Baraniuk1

Source URL: ankitbpatel715.files.wordpress.com

BUDVYTIS ET AL.: MOT FOR VIDEO SEGMENTATION 1 MoT - Mixture of Trees Probabilistic Graphical Model for Video Segmentation

Source URL: mi.eng.cam.ac.uk

Vol. 18 noPages 1194–1206 BIOINFORMATICS Bayesian infinite mixture model based clustering

Source URL: eh3.uc.edu

Research © 2011 by The American Society for Biochemistry and Molecular Biology, Inc. This paper is available on line at http://www.mcponline.org A Bayesian Mixture Model for Comparative Spectral Count Data in Shotgun Pr

Source URL: yulab.icmb.cornell.edu

Muscle Categorization using Quantitative Needle Electromyography A 2-Stage Gaussian Mixture Model based Approach Meena AbdelMaseeh∗ , Pascal Poupart† , Benn Smith‡ and Daniel Stashuk∗ ∗ Syst

Source URL: cs.uwaterloo.ca

WEYAND, DESELAERS, NEY: LOG-LINEAR MIXTURES 1 Log-Linear Mixtures for Object Class Recognition

Source URL: thomas.deselaers.de

• Machine learning
• Artificial intelligence
• Learning
• Statistics
• Mixture model
• Log-linear model
• Pattern recognition
• K-means clustering
• Linear classifier

Gaussian sampling by local perturbations George Papandreou Department of Statistics University of California, Los Angeles

Source URL: www.stat.ucla.edu

• Statistics
• Statistical models
• Stochastic processes
• Linear filters
• Bayesian statistics
• Estimation theory
• Gibbs sampling
• Normal distribution
• Kalman filter
• Mixture model
• Bayesian network
• Gaussian process

Learning stick-figure models using nonparametric Bayesian priors over trees Edward W. Meeds, David A. Ross, Richard S. Zemel, and Sam T. Roweis Department of Computer Science University of Toronto {ewm, dross, zemel, row

Source URL: www.cs.toronto.edu

• Statistics
• Probability
• Statistical theory
• Probability distributions
• Statistical models
• Bayesian statistics
• Stochastic processes
• Graphical models
• Bayesian network
• Gibbs sampling
• Dirichlet process
• Mixture model

EFFICIENT INITIALIZATION OF MIXTURES OF EXPERTS FOR HUMAN POSE ESTIMATION Huazhong Ning, Yuxiao Hu, Thomas Huang ECE Department, U. of Illinois at Urbana-Champaign, Urbana, IL 61801. {hning2,hu3, huang}@ifp.uiuc.edu ABS

Source URL: www.ifp.illinois.edu

• Statistics
• Applied mathematics
• Machine learning
• Learning
• Expectationmaximization algorithm
• Pattern recognition
• Regression analysis
• Mixture model
• Artificial neural network
• Perceptron