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IFIP12
2008
13 years 10 months ago
Bayesian Networks Optimization Based on Induction Learning Techniques
Obtaining a bayesian network from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper, we define an automatic learni...
Paola Britos, Pablo Felgaer, Ramón Garc&iac...
SIGKDD
2000
231views more  SIGKDD 2000»
13 years 8 months ago
KDD-99 Classifier Learning Contest: LLSoft's Results Overview
Kernel Miner is a new data-mining tool based on building the optimal decision forest. The tool won second place in the KDD'99 Classifier Learning Contest, August 1999. We des...
Itzhak Levin
CEAS
2007
Springer
14 years 24 days ago
Learning Fast Classifiers for Image Spam
Recently, spammers have proliferated "image spam", emails which contain the text of the spam message in a human readable image instead of the message body, making detect...
Mark Dredze, Reuven Gevaryahu, Ari Elias-Bachrach
ANNPR
2006
Springer
14 years 17 days ago
Hierarchical Neural Networks Utilising Dempster-Shafer Evidence Theory
Abstract. Hierarchical neural networks show many benefits when employed for classification problems even when only simple methods analogous to decision trees are used to retrieve t...
Rebecca Fay, Friedhelm Schwenker, Christian Thiel,...
ICDE
2010
IEEE
174views Database» more  ICDE 2010»
13 years 6 months ago
Semantic flooding: Search over semantic links
Abstract-- Classification hierarchies are trees where links codify the fact that a node lower in the hierarchy contains documents whose contents are more specific than those one le...
Fausto Giunchiglia, Uladzimir Kharkevich, Alethia ...