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» On Learning Decision Trees with Large Output Domains
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ESANN
2004
13 years 9 months ago
Data Mining Techniques on the Evaluation of Wireless Churn
This work focuses on one of the most critical issues to plague the wireless telecommunications industry today: the loss of a valuable subscriber to a competitor, also defined as ch...
Jorge Ferreira, Marley B. R. Vellasco, Marco Aur&e...
BIBM
2010
IEEE
139views Bioinformatics» more  BIBM 2010»
13 years 5 months ago
Scalable, updatable predictive models for sequence data
The emergence of data rich domains has led to an exponential growth in the size and number of data repositories, offering exciting opportunities to learn from the data using machin...
Neeraj Koul, Ngot Bui, Vasant Honavar
CORR
2000
Springer
120views Education» more  CORR 2000»
13 years 7 months ago
Scaling Up Inductive Logic Programming by Learning from Interpretations
When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming ...
Hendrik Blockeel, Luc De Raedt, Nico Jacobs, Bart ...
CORR
2002
Springer
142views Education» more  CORR 2002»
13 years 7 months ago
Learning Algorithms for Keyphrase Extraction
Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases ...
Peter D. Turney
FSKD
2007
Springer
98views Fuzzy Logic» more  FSKD 2007»
14 years 1 months ago
Learning Selective Averaged One-Dependence Estimators for Probability Estimation
Naïve Bayes is a well-known effective and efficient classification algorithm, but its probability estimation performance is poor. Averaged One-Dependence Estimators, simply AODE,...
Qing Wang, Chuan-hua Zhou, Jiankui Guo