Sciweavers

30 search results - page 4 / 6
» Map approach to learning sparse Gaussian Markov networks
Sort
View

Book
778views
15 years 3 months ago
Gaussian Processes for Machine Learning
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...
ICIC
2005
Springer
13 years 11 months ago
Sequential Stratified Sampling Belief Propagation for Multiple Targets Tracking
Rather than the difficulties of highly non-linear and non-Gaussian observation process and the state distribution in single target tracking, the presence of a large, varying number...
Jianru Xue, Nanning Zheng, Xiaopin Zhong
ACL
1998
13 years 7 months ago
A Connectionist Architecture for Learning to Parse
We present a connectionist architecture and demonstrate that it can learn syntactic parsing from a corpus of parsed text. The architecture can represent syntactic constituents, an...
James Henderson, Peter Lane
IJCAI
2003
13 years 7 months ago
A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data
This paper summarizes a probabilistic approach for localizing people through the signal strengths of a wireless IEEE 802.11b network. Our approach uses data labeled by ground trut...
Sebastian Thrun, Geoffrey J. Gordon, Frank Pfennin...
ACCV
2006
Springer
13 years 11 months ago
Tracking Targets Via Particle Based Belief Propagation
We first formulate multiple targets tracking problem in a dynamic Markov network(DMN)which is derived from a MRFs for joint target state and a binary process for occlusion of dual...
Jianru Xue, Nanning Zheng, Xiaopin Zhong