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» Approximate Learning of Dynamic Models
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NIPS
2008
15 years 3 months ago
Simple Local Models for Complex Dynamical Systems
We present a novel mathematical formalism for the idea of a "local model" of an uncontrolled dynamical system, a model that makes only certain predictions in only certai...
Erik Talvitie, Satinder Singh
ECML
2007
Springer
15 years 8 months ago
Imitation Learning Using Graphical Models
Imitation-based learning is a general mechanism for rapid acquisition of new behaviors in autonomous agents and robots. In this paper, we propose a new approach to learning by imit...
Deepak Verma, Rajesh P. N. Rao
AAAI
2008
15 years 4 months ago
Latent Tree Models and Approximate Inference in Bayesian Networks
We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and wh...
Yi Wang, Nevin Lianwen Zhang, Tao Chen
CSDA
2007
134views more  CSDA 2007»
15 years 1 months ago
Variational approximations in Bayesian model selection for finite mixture distributions
Variational methods for model comparison have become popular in the neural computing/machine learning literature. In this paper we explore their application to the Bayesian analys...
Clare A. McGrory, D. M. Titterington
TNN
2010
216views Management» more  TNN 2010»
14 years 8 months ago
Simplifying mixture models through function approximation
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we propose a general, structure-preserving approach to reduce its model complexity, w...
Kai Zhang, James T. Kwok