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» Optimization and Interpretation of Rule-based Classifiers
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KDD
2005
ACM
117views Data Mining» more  KDD 2005»
15 years 9 months ago
Rule extraction from linear support vector machines
We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike...
Glenn Fung, Sathyakama Sandilya, R. Bharat Rao
ICML
2004
IEEE
15 years 10 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. ...
87
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ICML
2010
IEEE
14 years 10 months ago
Boosting Classifiers with Tightened L0-Relaxation Penalties
We propose a novel boosting algorithm which improves on current algorithms for weighted voting classification by striking a better balance between classification accuracy and the ...
Noam Goldberg, Jonathan Eckstein
GECCO
2007
Springer
213views Optimization» more  GECCO 2007»
15 years 3 months ago
Genetically programmed learning classifier system description and results
An agent population can be evolved in a complex environment to perform various tasks and optimize its job performance using Learning Classifier System (LCS) technology. Due to the...
Gregory Anthony Harrison, Eric W. Worden
EUROGP
2005
Springer
156views Optimization» more  EUROGP 2005»
15 years 3 months ago
Evolving Rules for Document Classification
We describe a novel method for using Genetic Programming to create compact classification rules based on combinations of N-Grams (character strings). Genetic programs acquire fitne...
Laurence Hirsch, Masoud Saeedi, Robin Hirsch