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» A Minimum Relative Entropy Principle for Learning and Acting
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ICRA
2007
IEEE
224views Robotics» more  ICRA 2007»
14 years 2 months ago
Visual Categorization Robust to Large Intra-Class Variations using Entropy-guided Codebook
Abstract— Categorizing visual elements is fundamentally important for autonomous mobile robots to get intelligence such as new object acquisition and topological place classific...
Sungho Kim, In-So Kweon, Chil-Woo Lee
WACV
2007
IEEE
14 years 2 months ago
Object Categorization Robust to Surface Markings using Entropy-guided Codebook
Visual categorization is fundamentally important for autonomous mobile robots to get intelligence such as novel object acquisition and topological place recognition. The main dif...
Sungho Kim, In-So Kweon
DIS
1999
Springer
13 years 12 months ago
The Melting Pot of Automated Discovery: Principles for a New Science
After two decades of research on automated discovery, many principles are shaping up as a foundation of discovery science. In this paper we view discovery science as automation of ...
Jan M. Zytkow
JMLR
2011
192views more  JMLR 2011»
13 years 2 months ago
Minimum Description Length Penalization for Group and Multi-Task Sparse Learning
We propose a framework MIC (Multiple Inclusion Criterion) for learning sparse models based on the information theoretic Minimum Description Length (MDL) principle. MIC provides an...
Paramveer S. Dhillon, Dean P. Foster, Lyle H. Unga...
ICML
2007
IEEE
14 years 8 months ago
Minimum reference set based feature selection for small sample classifications
We address feature selection problems for classification of small samples and high dimensionality. A practical example is microarray-based cancer classification problems, where sa...
Xue-wen Chen, Jong Cheol Jeong