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» A Framework for Multiple-Instance Learning
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AI
2002
Springer
15 years 4 months ago
Learning Bayesian networks from data: An information-theory based approach
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
BC
2002
90views more  BC 2002»
15 years 4 months ago
What can the hippocampal representation of environmental geometry tell us about Hebbian learning?
The importance of the hippocampus in spatial representation is well established. It is suggested that the rodent hippocampal network should provide an optimal substrate for the stu...
Colin Lever, Neil Burgess, Francesca Cacucci, Tom ...
PAMI
2010
276views more  PAMI 2010»
15 years 2 months ago
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
—This paper considers feature selection for data classification in the presence of a huge number of irrelevant features. We propose a new feature selection algorithm that addres...
Yijun Sun, Sinisa Todorovic, Steve Goodison
PCM
2010
Springer
183views Multimedia» more  PCM 2010»
15 years 2 months ago
Fast H.264 Encoding Based on Statistical Learning
Abstract. In this paper, we propose an efficient video coding system that applies statistical learning methods to reduce the computational cost in H.264 encoder. The proposed metho...
Chen-Kuo Chiang, Shang-Hong Lai
127
Voted
CDC
2010
IEEE
113views Control Systems» more  CDC 2010»
14 years 11 months ago
Independent vs. joint estimation in multi-agent iterative learning control
This paper studies iterative learning control (ILC) in a multi-agent framework. A group of agents simultaneously and repeatedly perform the same task. The agents improve their perf...
Angela Schöllig, Javier Alonso-Mora, Raffaell...