1018 search results - page 123 / 204 » Learning Probabilistic Models of Contours |
ESSMAC
2003
Springer
15 years 3 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...
AIED
2009
Springer
15 years 4 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...
NIPS
2007
14 years 11 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 ...
IROS
2008
IEEE
15 years 4 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...
ICMCS
2006
IEEE
15 years 4 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-...