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» Nonlinear Predictive Control with a Gaussian Process Model
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125
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NIPS
2004
15 years 4 months ago
Joint Tracking of Pose, Expression, and Texture using Conditionally Gaussian Filters
We present a generative model and stochastic filtering algorithm for simultaneous tracking of 3D position and orientation, non-rigid motion, object texture, and background texture...
Tim K. Marks, John R. Hershey, J. Cooper Roddey, J...
128
Voted
UAI
2004
15 years 4 months ago
Dynamical Systems Trees
We propose dynamical systems trees (DSTs) as a flexible model for describing multiple processes that interact via a hierarchy of aggregating processes. DSTs extend nonlinear dynam...
Andrew Howard, Tony Jebara
108
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UAI
2004
15 years 4 months ago
Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences
We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of s...
Cosma Rohilla Shalizi, Kristina Lisa Shalizi
134
Voted
JMS
2010
90views more  JMS 2010»
15 years 1 months ago
Prediction of Clinical Conditions after Coronary Bypass Surgery using Dynamic Data Analysis
This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes ...
Kristien Van Loon, Fabián Güiza, Geert...
105
Voted
CSDA
2007
108views more  CSDA 2007»
15 years 2 months ago
Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm
Nonlinear random effects models with finite mixture structures are used to identify polymorphism in pharmacokinetic/ pharmacodynamic (PK/PD) phenotypes. An EM algorithm for maxim...
Xiaoning Wang, Alan Schumitzky, David Z. D'Argenio