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» Superset Learning Based on Generalized Loss Minimization
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ICML
2010
IEEE
14 years 11 months ago
Simple and Efficient Multiple Kernel Learning by Group Lasso
We consider the problem of how to improve the efficiency of Multiple Kernel Learning (MKL). In literature, MKL is often solved by an alternating approach: (1) the minimization of ...
Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, Mic...
ACL
2003
14 years 11 months ago
Fast Methods for Kernel-Based Text Analysis
Kernel-based learning (e.g., Support Vector Machines) has been successfully applied to many hard problems in Natural Language Processing (NLP). In NLP, although feature combinatio...
Taku Kudo, Yuji Matsumoto
ECML
2007
Springer
15 years 4 months ago
On Phase Transitions in Learning Sparse Networks
In this paper we study the identification of sparse interaction networks as a machine learning problem. Sparsity means that we are provided with a small data set and a high number...
Goele Hollanders, Geert Jan Bex, Marc Gyssens, Ron...
ISCIS
2005
Springer
15 years 3 months ago
Classification of Volatile Organic Compounds with Incremental SVMs and RBF Networks
Support Vector Machines (SVMs) have been applied to solve the classification of volatile organic compounds (VOC) data in some recent studies. SVMs provide good generalization perfo...
Zeki Erdem, Robi Polikar, Nejat Yumusak, Fikret S....
EWSN
2009
Springer
15 years 10 months ago
Potentials of Opportunistic Routing in Energy-Constrained Wireless Sensor Networks
The low quality of wireless links leads to perpetual packet losses. While an acknowledgment mechanism is generally used to cope with these losses, multiple retransmissions neverthe...
Gunnar Schaefer, François Ingelrest, Martin...