247 search results - page 15 / 50 » Superset Learning Based on Generalized Loss Minimization |
101
Voted
COLT
2001
Springer
15 years 4 months ago
2001
Springer
We consider geometric conditions on a labeled data set which guarantee that boosting algorithms work well when linear classifiers are used as weak learners. We start by providing ...
115
Voted
KDD
2004
ACM
16 years 24 days ago
2004
ACM
Co-clustering is a powerful data mining technique with varied applications such as text clustering, microarray analysis and recommender systems. Recently, an informationtheoretic ...
109
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ICDM
2009
IEEE
15 years 7 months ago
2009
IEEE
—We describe parallel methods for solving large-scale, high-dimensional, sparse least-squares problems that arise in machine learning applications such as document classificatio...
115
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ICML
2007
IEEE
16 years 1 months ago
2007
IEEE
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
94
Voted
ICML
2004
IEEE
16 years 1 months ago
2004
IEEE
In this paper we show that many kernel methods can be adapted to deal with indefinite kernels, that is, kernels which are not positive semidefinite. They do not satisfy Mercer...