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ICPR
2008
IEEE
16 years 4 months ago
Multiple kernel learning from sets of partially matching image features
Abstract: Kernel classifiers based on Support Vector Machines (SVM) have achieved state-ofthe-art results in several visual classification tasks, however, recent publications and d...
Guo ShengYang, Min Tan, Si-Yao Fu, Zeng-Guang Hou,...
ACIVS
2009
Springer
15 years 10 months ago
Image Categorization Using ESFS: A New Embedded Feature Selection Method Based on SFS
Abstract. Feature subset selection is an important subject when training classifiers in Machine Learning (ML) problems. Too many input features in a ML problem may lead to the so-...
Huanzhang Fu, Zhongzhe Xiao, Emmanuel Dellandr&eac...
148
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BMCBI
2006
128views more  BMCBI 2006»
15 years 3 months ago
New directions in biomedical text annotation: definitions, guidelines and corpus construction
Background: While biomedical text mining is emerging as an important research area, practical results have proven difficult to achieve. We believe that an important first step tow...
W. John Wilbur, Andrey Rzhetsky, Hagit Shatkay
132
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GECCO
2007
Springer
194views Optimization» more  GECCO 2007»
15 years 9 months ago
Hybrid coevolutionary algorithms vs. SVM algorithms
As a learning method support vector machine is regarded as one of the best classifiers with a strong mathematical foundation. On the other hand, evolutionary computational techniq...
Rui Li, Bir Bhanu, Krzysztof Krawiec
ICML
2004
IEEE
16 years 4 months ago
Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm
In kernel methods, an interesting recent development seeks to learn a good kernel from empirical data automatically. In this paper, by regarding the transductive learning of the k...
Zhihua Zhang, Dit-Yan Yeung, James T. Kwok