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» Evaluating learning algorithms and classifiers
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GFKL
2007
Springer
158views Data Mining» more  GFKL 2007»
15 years 7 months ago
Investigating Classifier Learning Behavior with Experiment Databases
Experimental assessment of the performance of classification algorithms is an important aspect of their development and application on real-world problems. To facilitate this analy...
Joaquin Vanschoren, Hendrik Blockeel
169
Voted
JMLR
2010
185views more  JMLR 2010»
14 years 10 months ago
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
We introduce a simple order-based greedy heuristic for learning discriminative structure within generative Bayesian network classifiers. We propose two methods for establishing an...
Franz Pernkopf, Jeff A. Bilmes
ICML
2004
IEEE
16 years 4 months ago
Learning large margin classifiers locally and globally
A new large margin classifier, named MaxiMin Margin Machine (M4 ) is proposed in this paper. This new classifier is constructed based on both a "local" and a "globa...
Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. ...
ADBIS
1999
Springer
104views Database» more  ADBIS 1999»
15 years 8 months ago
Arbiter Meta-Learning with Dynamic Selection of Classifiers and Its Experimental Investigation
In data mining, the selection of an appropriate classifier to estimate the value of an unknown attribute for a new instance has an essential impact to the quality of the classifica...
Alexey Tsymbal, Seppo Puuronen, Vagan Y. Terziyan
142
Voted
ICML
2005
IEEE
16 years 4 months ago
Building Sparse Large Margin Classifiers
This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The ad...
Bernhard Schölkopf, Gökhan H. Bakir, Min...