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» Approximate algorithms for neural-Bayesian approaches
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NIPS
2008
14 years 11 months ago
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switc...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
UAI
2004
14 years 11 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
WSC
2004
14 years 11 months ago
Exploiting Temporal Uncertainty in Process-Oriented Distributed Simulations
Existing research has defined a new type of simulation time called Approximate Time, where the simulation's knowledge about the values that represent time is uncertain. The a...
Margaret L. Loper, Richard M. Fujimoto
NIPS
1996
14 years 11 months ago
Early Brain Damage
Optimal Brain Damage (OBD) is a method for reducing the number of weights in a neural network. OBD estimates the increase in cost function if weights are pruned and is a valid app...
Volker Tresp, Ralph Neuneier, Hans-Georg Zimmerman...
AAAI
1990
14 years 11 months ago
Tree Decomposition with Applications to Constraint Processing
This paper concerns the task of removing redundant information from a given knowledge base, and restructuring it in the form of a tree, so as to admit efficient problem solving ro...
Itay Meiri, Judea Pearl, Rina Dechter