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KDD
1997
ACM
142views Data Mining» more  KDD 1997»
14 years 3 days ago
Applying Data Mining and Machine Learning Techniques to Submarine Intelligence Analysis
Wedescribehow specializeddatabasetechnology and data analysis methods were applied by the Swedish defense to help deal with the violation of Swedish marine territory by foreign su...
Ulla Bergsten, Johan Schubert, Per Svensson
ICML
2008
IEEE
14 years 8 months ago
Discriminative parameter learning for Bayesian networks
Bayesian network classifiers have been widely used for classification problems. Given a fixed Bayesian network structure, parameters learning can take two different approaches: ge...
Jiang Su, Harry Zhang, Charles X. Ling, Stan Matwi...
BMCBI
2007
91views more  BMCBI 2007»
13 years 8 months ago
A machine learning approach for the identification of odorant binding proteins from sequence-derived properties
Background: Odorant binding proteins (OBPs) are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as od...
Ganesan Pugalenthi, E. Ke Tang, Ponnuthurai N. Sug...
ICML
2006
IEEE
14 years 8 months ago
Learning user preferences for sets of objects
Most work on preference learning has focused on pairwise preferences or rankings over individual items. In this paper, we present a method for learning preferences over sets of it...
Marie desJardins, Eric Eaton, Kiri Wagstaff
DIS
2009
Springer
14 years 2 months ago
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio
Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...
Yasser El-Manzalawy, Vasant Honavar