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» Domain equations for probabilistic processes
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UAI
1998
15 years 3 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
114
Voted
UAI
2000
15 years 3 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
15 years 7 months 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
15 years 8 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
15 years 6 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...