1018 search results - page 123 / 204 » Learning Probabilistic Models of Contours |
130
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ESSMAC
2003
Springer
15 years 9 months ago
2003
Springer
Abstract. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can h...
113
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AIED
2009
Springer
15 years 10 months ago
2009
Springer
We present a novel phoneme-based student model for spelling training. Our model is data driven, adapts to the user and provides information for, e.g., optimal word selection. We de...
117
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NIPS
2007
15 years 5 months ago
2007
It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements. We assume that the entire matrix is a single sample drawn ...
146
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IROS
2008
IEEE
15 years 10 months ago
2008
IEEE
Abstract— Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filt...
161
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ICMCS
2006
IEEE
15 years 10 months ago
2006
IEEE
In this paper, we propose a probabilistic model for web image mining, which is based on concept-sensitive salient regions without human intervene. Our goal is to achieve a middle-...