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» Predicting Success in Machine Translation
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BMCBI
2007
116views more  BMCBI 2007»
13 years 8 months ago
MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes
Background: Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures pre...
Huaiqiu Zhu, Gang-Qing Hu, Yi-Fan Yang, Jin Wang, ...
ACL
2006
13 years 10 months ago
Learning to Predict Case Markers in Japanese
Japanese case markers, which indicate the grammatical relation of the complement NP to the predicate, often pose challenges to the generation of Japanese text, be it done by a for...
Hisami Suzuki, Kristina Toutanova
CIBCB
2009
IEEE
13 years 9 months ago
Application of machine learning approaches on quantitative structure activity relationships
Machine Learning techniques are successfully applied to establish quantitative relations between chemical structure and biological activity (QSAR), i.e. classify compounds as activ...
Mariusz Butkiewicz, Ralf Mueller, Danilo Selic, Er...
SAT
2009
Springer
111views Hardware» more  SAT 2009»
14 years 3 months ago
Restart Strategy Selection Using Machine Learning Techniques
Abstract. Restart strategies are an important factor in the performance of conflict-driven Davis Putnam style SAT solvers. Selecting a good restart strategy for a problem instance...
Shai Haim, Toby Walsh
ACMSE
2006
ACM
14 years 2 months ago
Support vector machines for collaborative filtering
Support Vector Machines (SVMs) have successfully shown efficiencies in many areas such as text categorization. Although recommendation systems share many similarities with text ca...
Zhonghang Xia, Yulin Dong, Guangming Xing