509 search results - page 24 / 102 » Using Learning for Approximation in Stochastic Processes |
ESSMAC
2003
Springer
14 years 19 days ago
2003
Springer
Abstract. While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational com...
ACCV
2007
Springer
14 years 1 months ago
2007
Springer
Abstract. Computation time is an important performance characteristic of computer vision algorithms. This paper shows how existing (slow) binary-valued decision algorithms can be a...
UAI
1998
13 years 8 months ago
1998
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...
SAB
2010
Springer
13 years 5 months ago
2010
Springer
Abstract. In this paper we study distributed online learning of locomotion gaits for modular robots. The learning is based on a stochastic approximation method, SPSA, which optimiz...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
This paper explores the issue of recognizing, generalizing and reproducing arbitrary gestures. We aim at extracting a representation that encapsulates only the key aspects of the ...