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» Domain equations for probabilistic processes
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UAI
1998
13 years 9 months ago
Learning the Structure of Dynamic Probabilistic Networks
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend stru...
Nir Friedman, Kevin P. Murphy, Stuart J. Russell
UAI
2000
13 years 9 months ago
Probabilistic State-Dependent Grammars for Plan Recognition
Techniques for plan recognition under uncertainty require a stochastic model of the plangeneration process. We introduce probabilistic state-dependent grammars (PSDGs) to represen...
David V. Pynadath, Michael P. Wellman
IEEEICCI
2003
IEEE
14 years 25 days ago
Signal Classification through Multifractal Analysis and Complex Domain Neural Networks
This paper describes a system capable of classifying stochastic, self-affine, nonstationary signals produced by nonlinear systems. The classification and analysis of these signals...
Witold Kinsner, V. Cheung, K. Cannons, J. Pear, T....
ICASSP
2008
IEEE
14 years 2 months ago
Image inpainting with a wavelet domain Hidden Markov tree model
We present a novel technique for image inpainting, the problem of filling-in missing image parts. Image inpainting is ill-posed and we adopt a probabilistic model-based approach ...
George Papandreou, Petros Maragos, Anil Kokaram
ICPR
2000
IEEE
13 years 12 months ago
Stochastic Error-Correcting Parsing for OCR Post-Processing
In this paper, stochastic error-correcting parsing is proposed as a powerful and flexible method to post-process the results of an optical character recognizer (OCR). Determinist...
Juan Carlos Pérez-Cortes, Juan-Carlos Ameng...