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» Online Ensemble Learning: An Empirical Study
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ECML
2005
Springer
14 years 1 months ago
Error-Sensitive Grading for Model Combination
Abstract. Ensemble learning is a powerful learning approach that combines multiple classifiers to improve prediction accuracy. An important decision while using an ensemble of cla...
Surendra K. Singhi, Huan Liu
AI
2002
Springer
13 years 7 months ago
Ensembling neural networks: Many could be better than all
Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. In this paper, the relationship between the ensemble and its compone...
Zhi-Hua Zhou, Jianxin Wu, Wei Tang
CP
2007
Springer
14 years 1 months ago
On Universal Restart Strategies for Backtracking Search
Abstract. Constraint satisfaction and propositional satisfiability problems are often solved using backtracking search. Previous studies have shown that a technique called randomi...
Huayue Wu, Peter van Beek
FLAIRS
2008
13 years 10 months ago
Machine Learning to Predict the Incidence of Retinopathy of Prematurity
Retinopathy of Prematurity (ROP) is a disorder afflicting prematurely born infants. ROP can be positively diagnosed a few weeks after birth. The goal of this study is to build an ...
Aniket Ray, Vikas Kumar, Balaraman Ravindran, Ling...
ICAI
2004
13 years 9 months ago
A Comparison of Resampling Methods for Clustering Ensembles
-- Combination of multiple clusterings is an important task in the area of unsupervised learning. Inspired by the success of supervised bagging algorithms, we propose a resampling ...
Behrouz Minaei-Bidgoli, Alexander P. Topchy, Willi...