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COLT
2005
Springer
14 years 1 months ago
2005
Springer
A general model is proposed for studying ranking problems. We investigate learning methods based on empirical minimization of the natural estimates of the ranking risk. The empiric...
ESANN
2007
13 years 9 months ago
2007
One-class support vector machines (1-SVMs) estimate the level set of the underlying density observed data. Aside the kernel selection issue, one difficulty concerns the choice of t...
NIPS
2007
13 years 9 months ago
2007
This paper1 explores the use of a Maximal Average Margin (MAM) optimality principle for the design of learning algorithms. It is shown that the application of this risk minimizati...
NIPS
2003
13 years 9 months ago
2003
Margin maximizing properties play an important role in the analysis of classi£cation models, such as boosting and support vector machines. Margin maximization is theoretically in...
JMLR
2006
13 years 7 months ago
2006
Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM ...