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» Set cover algorithms for very large datasets
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ICDAR
2007
IEEE
16 years 1 months ago
A Sparse and Locally Shift Invariant Feature Extractor Applied to Document Images
We describe an unsupervised learning algorithm for extracting sparse and locally shift-invariant features. We also devise a principled procedure for learning hierarchies of invari...
Marc'Aurelio Ranzato, Yann LeCun
ICCS
2005
Springer
16 years 8 days ago
Phylogenetic Networks, Trees, and Clusters
Phylogenetic networks model evolutionary histories in the presence of non-treelike events such as hybrid speciation and horizontal gene transfer. In spite of their widely acknowled...
Luay Nakhleh, Li-San Wang
CIKM
2008
Springer
15 years 8 months ago
The role of syntactic features in protein interaction extraction
Most approaches for protein interaction mining from biomedical texts use both lexical and syntactic features. However, the individual impact of these two kinds of features on the ...
Timur Fayruzov, Martine De Cock, Chris Cornelis, V...
GECCO
2005
Springer
228views Optimization» more  GECCO 2005»
16 years 8 days ago
Applying metaheuristic techniques to search the space of bidding strategies in combinatorial auctions
Many non-cooperative settings that could potentially be studied using game theory are characterized by having very large strategy spaces and payoffs that are costly to compute. Be...
Ashish Sureka, Peter R. Wurman
155
Voted
WEBI
2005
Springer
16 years 7 days ago
Improving Web Clustering by Cluster Selection
Web page clustering is a technology that puts semantically related web pages into groups and is useful for categorizing, organizing, and refining search results. When clustering ...
Daniel Crabtree, Xiaoying Gao, Peter Andreae