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» Learning Approaches to Wrapper Induction
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147
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ML
2000
ACM
154views Machine Learning» more  ML 2000»
15 years 1 months ago
Lazy Learning of Bayesian Rules
The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. A numb...
Zijian Zheng, Geoffrey I. Webb
GECCO
2008
Springer
232views Optimization» more  GECCO 2008»
15 years 3 months ago
An efficient SVM-GA feature selection model for large healthcare databases
This paper presents an efficient hybrid feature selection model based on Support Vector Machine (SVM) and Genetic Algorithm (GA) for large healthcare databases. Even though SVM an...
Rick Chow, Wei Zhong, Michael Blackmon, Richard St...
EENERGY
2010
15 years 5 months ago
Towards energy-aware scheduling in data centers using machine learning
As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to find better an...
Josep Lluis Berral, Iñigo Goiri, Ramon Nou,...
123
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PKDD
2007
Springer
146views Data Mining» more  PKDD 2007»
15 years 8 months ago
A Method for Multi-relational Classification Using Single and Multi-feature Aggregation Functions
This paper presents a novel method for multi-relational classification via an aggregation-based Inductive Logic Programming (ILP) approach. We extend the classical ILP representati...
Richard Frank, Flavia Moser, Martin Ester
108
Voted
IJCAI
2007
15 years 3 months ago
Real Boosting a la Carte with an Application to Boosting Oblique Decision Tree
In the past ten years, boosting has become a major field of machine learning and classification. This paper brings contributions to its theory and algorithms. We first unify a ...
Claudia Henry, Richard Nock, Frank Nielsen