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ICML
2005
IEEE
16 years 2 months ago
Variational Bayesian image modelling
We present a variational Bayesian framework for performing inference, density estimation and model selection in a special class of graphical models--Hidden Markov Random Fields (H...
Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang
ECML
2007
Springer
15 years 7 months ago
Principal Component Analysis for Large Scale Problems with Lots of Missing Values
Abstract. Principal component analysis (PCA) is a well-known classical data analysis technique. There are a number of algorithms for solving the problem, some scaling better than o...
Tapani Raiko, Alexander Ilin, Juha Karhunen
ML
2010
ACM
151views Machine Learning» more  ML 2010»
14 years 12 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
SEAL
1998
Springer
15 years 5 months ago
Evolutionary Programming-Based Uni-vector Field Method for Fast Mobile Robot Navigation
Most of the obstacle avoidance techniques do not consider the robot orientation or its nal angle at the target position. These techniques deal with the robot position only and are ...
Yong-Jae Kim, Dong-Han Kim, Jong-Hwan Kim
MM
2004
ACM
219views Multimedia» more  MM 2004»
15 years 7 months ago
Multi-level annotation of natural scenes using dominant image components and semantic concepts
Automatic image annotation is a promising solution to enable semantic image retrieval via keywords. In this paper, we propose a multi-level approach to annotate the semantics of n...
Jianping Fan, Yuli Gao, Hangzai Luo