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ALT
2003
Springer
14 years 19 days ago
Can Learning in the Limit Be Done Efficiently?
Abstract. Inductive inference can be considered as one of the fundamental paradigms of algorithmic learning theory. We survey results recently obtained and show their impact to pot...
Thomas Zeugmann
ICML
2003
IEEE
14 years 9 months ago
Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning
We present a novel Bayesian approach to the problem of value function estimation in continuous state spaces. We define a probabilistic generative model for the value function by i...
Yaakov Engel, Shie Mannor, Ron Meir
KDD
2009
ACM
219views Data Mining» more  KDD 2009»
14 years 9 months ago
Structured correspondence topic models for mining captioned figures in biological literature
A major source of information (often the most crucial and informative part) in scholarly articles from scientific journals, proceedings and books are the figures that directly pro...
Amr Ahmed, Eric P. Xing, William W. Cohen, Robert ...
ECCV
2006
Springer
14 years 11 months ago
A Learning Based Approach for 3D Segmentation and Colon Detagging
Abstract. Foreground and background segmentation is a typical problem in computer vision and medical imaging. In this paper, we propose a new learning based approach for 3D segment...
Zhuowen Tu, Xiang Zhou, Dorin Comaniciu, Luca Bogo...
ICML
2010
IEEE
13 years 7 months ago
Approximate Predictive Representations of Partially Observable Systems
We provide a novel view of learning an approximate model of a partially observable environment from data and present a simple implemenf the idea. The learned model abstracts away ...
Monica Dinculescu, Doina Precup