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SDM
2012
SIAM
234views Data Mining» more  SDM 2012»
7 years 10 months ago
On Evaluation of Outlier Rankings and Outlier Scores
Outlier detection research is currently focusing on the development of new methods and on improving the computation time for these methods. Evaluation however is rather heuristic,...
Erich Schubert, Remigius Wojdanowski, Arthur Zimek...
SIGMOD
2011
ACM
269views Database» more  SIGMOD 2011»
8 years 11 months ago
Advancing data clustering via projective clustering ensembles
Projective Clustering Ensembles (PCE) are a very recent advance in data clustering research which combines the two powerful tools of clustering ensembles and projective clustering...
Francesco Gullo, Carlotta Domeniconi, Andrea Tagar...
TSMC
2008
132views more  TSMC 2008»
9 years 8 months ago
Ensemble Algorithms in Reinforcement Learning
This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and fin...
Marco A. Wiering, Hado van Hasselt
ESANN
2006
9 years 9 months ago
Rotation-based ensembles of RBF networks
Abstract. Ensemble methods allow to improve the accuracy of classification methods. This work considers the application of one of these methods, named Rotation-based, when the clas...
Juan José Rodríguez, Jesús Ma...
MCS
2000
Springer
9 years 11 months ago
Ensemble Methods in Machine Learning
Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a (weighted) vote of their predictions. The original ensembl...
Thomas G. Dietterich
ECCV
2006
Springer
9 years 11 months ago
Comparing Ensembles of Learners: Detecting Prostate Cancer from High Resolution MRI
While learning ensembles have been widely used for various pattern recognition tasks, surprisingly, they have found limited application in problems related to medical image analysi...
Anant Madabhushi, Jianbo Shi, Michael D. Feldman, ...
KDD
2010
ACM
224views Data Mining» more  KDD 2010»
9 years 12 months ago
Ensemble pruning via individual contribution ordering
An ensemble is a set of learned models that make decisions collectively. Although an ensemble is usually more accurate than a single learner, existing ensemble methods often tend ...
Zhenyu Lu, Xindong Wu, Xingquan Zhu, Josh Bongard
ECML
2007
Springer
10 years 11 hour ago
Ensembles of Multi-Objective Decision Trees
Abstract. Ensemble methods are able to improve the predictive performance of many base classifiers. Up till now, they have been applied to classifiers that predict a single target ...
Dragi Kocev, Celine Vens, Jan Struyf, Saso Dzerosk...
PAKDD
2010
ACM
151views Data Mining» more  PAKDD 2010»
10 years 26 days ago
Ensemble Learning Based on Multi-Task Class Labels
Abstract. It is well known that diversity among component classifiers is crucial for constructing a strong ensemble. Most existing ensemble methods achieve this goal through resam...
Qing Wang, Liang Zhang
WAIM
2004
Springer
10 years 1 months ago
An Empirical Study of Building Compact Ensembles
Abstract. Ensemble methods can achieve excellent performance relying on member classifiers’ accuracy and diversity. We conduct an empirical study of the relationship of ensemble...
Huan Liu, Amit Mandvikar, Jigar Mody
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