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» A Structured Set of Higher-Order Problems
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JMLR
2012
13 years 6 months ago
Perturbation based Large Margin Approach for Ranking
We consider the task of devising large-margin based surrogate losses for the learning to rank problem. In this learning to rank setting, the traditional hinge loss for structured ...
Eunho Yang, Ambuj Tewari, Pradeep D. Ravikumar
SDM
2010
SIAM
204views Data Mining» more  SDM 2010»
15 years 5 months ago
Scalable Tensor Factorizations with Missing Data
The problem of missing data is ubiquitous in domains such as biomedical signal processing, network traffic analysis, bibliometrics, social network analysis, chemometrics, computer...
Evrim Acar, Daniel M. Dunlavy, Tamara G. Kolda, Mo...
CORR
2011
Springer
215views Education» more  CORR 2011»
14 years 7 months ago
Lasserre Hierarchy, Higher Eigenvalues, and Approximation Schemes for Quadratic Integer Programming with PSD Objectives
We present an approximation scheme for optimizing certain Quadratic Integer Programming problems with positive semidefinite objective functions and global linear constraints. Thi...
Venkatesan Guruswami, Ali Kemal Sinop
JMLR
2008
230views more  JMLR 2008»
15 years 4 months ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
ICPR
2002
IEEE
16 years 5 months ago
Prototype Selection for Finding Efficient Representations of Dissimilarity Data
The nearest neighbor (NN) rule is a simple and intuitive method for solving classification problems. Originally, it uses distances to the complete training set. It performs well, ...
Elzbieta Pekalska, Robert P. W. Duin