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PAKDD
2000
ACM
15 years 1 months ago
2000
ACM
The objective of data reduction is to obtain a compact representation of a large data set to facilitate repeated use of non-redundant information with complex and slow learning alg...
67
Voted
ICML
2005
IEEE
15 years 10 months ago
2005
IEEE
Maximum Margin Matrix Factorization (MMMF) was recently suggested (Srebro et al., 2005) as a convex, infinite dimensional alternative to low-rank approximations and standard facto...
83
Voted
ML
2008
ACM
14 years 9 months ago
2008
ACM
Abstract. Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful lear...
JMLR
2012
13 years 2 days ago
2012
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 ...
ICMLA
2009
14 years 7 months ago
2009
In structured prediction problems, outputs are not confined to binary labels; they are often complex objects such as sequences, trees, or alignments. Support Vector Machine (SVM) ...