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» A dual coordinate descent method for large-scale linear SVM
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ICML
2010
IEEE
13 years 6 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...
ICML
2010
IEEE
13 years 6 months ago
Learning Efficiently with Approximate Inference via Dual Losses
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
CVPR
2012
IEEE
11 years 8 months ago
Steerable part models
We describe a method for learning steerable deformable part models. Our models exploit the fact that part templates can be written as linear filter banks. We demonstrate that one...
Hamed Pirsiavash, Deva Ramanan
NIPS
2007
13 years 7 months ago
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations
We present a novel message passing algorithm for approximating the MAP problem in graphical models. The algorithm is similar in structure to max-product but unlike max-product it ...
Amir Globerson, Tommi Jaakkola