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» Evaluating learning algorithms and classifiers
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ICML
2004
IEEE
16 years 5 months ago
A hierarchical method for multi-class support vector machines
We introduce a framework, which we call Divide-by-2 (DB2), for extending support vector machines (SVM) to multi-class problems. DB2 offers an alternative to the standard one-again...
Volkan Vural, Jennifer G. Dy
122
Voted
IJHIS
2008
84views more  IJHIS 2008»
15 years 4 months ago
Selective generation of training examples in active meta-learning
Meta-Learning has been successfully applied to acquire knowledge used to support the selection of learning algorithms. Each training example in Meta-Learning (i.e. each meta-exampl...
Ricardo Bastos Cavalcante Prudêncio, Teresa ...
MICAI
2007
Springer
15 years 10 months ago
Weighted Instance-Based Learning Using Representative Intervals
Instance-based learning algorithms are widely used due to their capacity to approximate complex target functions; however, the performance of this kind of algorithms degrades signi...
Octavio Gómez, Eduardo F. Morales, Jes&uacu...
COLT
2010
Springer
15 years 2 months ago
Active Learning on Trees and Graphs
We investigate the problem of active learning on a given tree whose nodes are assigned binary labels in an adversarial way. Inspired by recent results by Guillory and Bilmes, we c...
Nicolò Cesa-Bianchi, Claudio Gentile, Fabio...
160
Voted
ICDCS
2000
IEEE
15 years 9 months ago
The Effect of Nogood Learning in Distributed Constraint Satisfaction
We present resolvent-based learning as a new nogood learning method for a distributed constraint satisfaction algorithm. This method is based on a look-back technique in constrain...
Makoto Yokoo, Katsutoshi Hirayama