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» A Parallel Mixture of SVMs for Very Large Scale Problems
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CORR
2010
Springer
130views Education» more  CORR 2010»
13 years 5 months ago
Approximated Structured Prediction for Learning Large Scale Graphical Models
In this paper we propose an approximated structured prediction framework for large scale graphical models and derive message-passing algorithms for learning their parameters effic...
Tamir Hazan, Raquel Urtasun
CORR
2008
Springer
142views Education» more  CORR 2008»
13 years 5 months ago
A Gaussian Belief Propagation Solver for Large Scale Support Vector Machines
Support vector machines (SVMs) are an extremely successful type of classification and regression algorithms. Building an SVM entails solving a constrained convex quadratic program...
Danny Bickson, Elad Yom-Tov, Danny Dolev
ICDCS
2010
IEEE
13 years 3 months ago
'Ethernet on AIR': Scalable Routing in very Large Ethernet-Based Networks
—Networks based on Ethernet bridging scale poorly as bridges flood the entire network repeatedly, and several schemes have been proposed to mitigate this flooding problem; howe...
Dhananjay Sampath, Suchit Agarwal, J. J. Garcia-Lu...
KDD
2008
ACM
178views Data Mining» more  KDD 2008»
14 years 5 months ago
Training structural svms with kernels using sampled cuts
Discriminative training for structured outputs has found increasing applications in areas such as natural language processing, bioinformatics, information retrieval, and computer ...
Chun-Nam John Yu, Thorsten Joachims
TNN
2008
182views more  TNN 2008»
13 years 5 months ago
Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines
Abstract--Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) ...
Ivor Wai-Hung Tsang, András Kocsor, James T...