1183 search results - page 76 / 237 » Learning Process Models with Missing Data |
136
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ICASSP
2010
IEEE
15 years 1 months ago
2010
IEEE
Sparse representation theory has been increasingly used in the fields of signal processing and machine learning. The standard sparse models are not invariant to spatial transform...
139
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AAAI
2000
15 years 2 months ago
2000
Due to the large variation and richness of visual inputs, statistical learning gets more and more concerned in the practice of visual processing such as visual tracking and recogn...
107
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NIPS
2004
15 years 2 months ago
2004
First-order Markov models have been successfully applied to many problems, for example in modeling sequential data using Markov chains, and modeling control problems using the Mar...
ICASSP
2011
IEEE
14 years 5 months ago
2011
IEEE
A dependent hierarchical beta process (dHBP) is developed as a prior for data that may be represented in terms of a sparse set of latent features (dictionary elements), with covar...
116
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PKDD
2010
Springer
14 years 12 months ago
2010
Springer
Abstract. Bayesian reinforcement learning (RL) is aimed at making more efficient use of data samples, but typically uses significantly more computation. For discrete Markov Decis...