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» Constraint Programming for Data Mining and Machine Learning
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ICANN
2009
Springer
15 years 7 months ago
Constrained Learning Vector Quantization or Relaxed k-Separability
Neural networks and other sophisticated machine learning algorithms frequently miss simple solutions that can be discovered by a more constrained learning methods. Transition from ...
Marek Grochowski, Wlodzislaw Duch
ICML
2007
IEEE
16 years 3 months ago
Multiple instance learning for sparse positive bags
We present a new approach to multiple instance learning (MIL) that is particularly effective when the positive bags are sparse (i.e. contain few positive instances). Unlike other ...
Razvan C. Bunescu, Raymond J. Mooney
AII
1992
15 years 7 months ago
Learning from Multiple Sources of Inaccurate Data
Most theoretical models of inductive inference make the idealized assumption that the data available to a learner is from a single and accurate source. The subject of inaccuracies ...
Ganesh Baliga, Sanjay Jain, Arun Sharma
ICML
2004
IEEE
15 years 8 months ago
Gradient LASSO for feature selection
LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the L1 penalty, the optim...
Yongdai Kim, Jinseog Kim
JMLR
2010
145views more  JMLR 2010»
14 years 10 months ago
Event based text mining for integrated network construction
The scientific literature is a rich and challenging data source for research in systems biology, providing numerous interactions between biological entities. Text mining technique...
Yvan Saeys, Sofie Van Landeghem, Yves Van de Peer