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104
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NAACL
2010
14 years 10 months ago
Unsupervised Model Adaptation using Information-Theoretic Criterion
In this paper we propose a novel general framework for unsupervised model adaptation. Our method is based on entropy which has been used previously as a regularizer in semi-superv...
Ariya Rastrow, Frederick Jelinek, Abhinav Sethy, B...
WACV
2005
IEEE
15 years 6 months ago
Using Co-Occurrence and Segmentation to Learn Feature-Based Object Models from Video
A number of recent systems for unsupervised featurebased learning of object models take advantage of cooccurrence: broadly, they search for clusters of discriminative features tha...
Thomas S. Stepleton, Tai Sing Lee
88
Voted
IJON
2006
99views more  IJON 2006»
15 years 21 days ago
Learning vector quantization: The dynamics of winner-takes-all algorithms
Winner-Takes-All (WTA) prescriptions for Learning Vector Quantization (LVQ) are studied in the framework of a model situation: Two competing prototype vectors are updated accordin...
Michael Biehl, Anarta Ghosh, Barbara Hammer
96
Voted
ICPR
2004
IEEE
16 years 1 months ago
Unsupervised Band Selection for Multispectral Images using Information Theory
In this paper, the implication of the relations of information in the case of multispectral images is analyzed. Higher-order mutual information can adopt positive or negative valu...
Arnoud C. Klaren, Filiberto Pla, José Mart&...
109
Voted
ASWC
2009
Springer
15 years 5 months ago
Semantic-Linguistic Feature Vectors for Search: Unsupervised Construction and Experimental Validation
Abstract. In this paper, we elaborate on an approach to construction of semantic-linguistic feature vectors (FV) that are used in search. These FVs are built based on domain semant...
Stein L. Tomassen, Darijus Strasunskas