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DAWAK
2006
Springer
13 years 11 months ago
Learning Classifiers from Distributed, Ontology-Extended Data Sources
Abstract. There is an urgent need for sound approaches to integrative and collaborative analysis of large, autonomous (and hence, inevitably semantically heterogeneous) data source...
Doina Caragea, Jun Zhang 0002, Jyotishman Pathak, ...
ICPR
2010
IEEE
13 years 11 months ago
Inverse Multiple Instance Learning for Classifier Grids
Abstract--Recently, classifier grids have shown to be a considerable alternative for object detection from static cameras. However, one drawback of such approaches is drifting if a...
Sabine Sternig, Peter M. Roth, Horst Bischof
KDD
2008
ACM
137views Data Mining» more  KDD 2008»
14 years 8 months ago
Learning classifiers from only positive and unlabeled data
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
Charles Elkan, Keith Noto
ICPR
2002
IEEE
14 years 9 months ago
Learning Bayesian Network Classifiers for Credit Scoring Using Markov Chain Monte Carlo Search
In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and co...
Bart Baesens, Michael Egmont-Petersen, Robert Cast...
GECCO
2007
Springer
213views Optimization» more  GECCO 2007»
14 years 2 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