Titlebook: Applications of Supervised and Unsupervised Ensemble Methods; Oleg Okun,Giorgio Valentini Book 2009 Springer-Verlag Berlin Heidelberg 2009

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书目名称Applications of Supervised and Unsupervised Ensemble Methods影响因子(影响力)




书目名称Applications of Supervised and Unsupervised Ensemble Methods影响因子(影响力)学科排名




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书目名称Applications of Supervised and Unsupervised Ensemble Methods网络公开度学科排名




书目名称Applications of Supervised and Unsupervised Ensemble Methods被引频次




书目名称Applications of Supervised and Unsupervised Ensemble Methods被引频次学科排名




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书目名称Applications of Supervised and Unsupervised Ensemble Methods年度引用学科排名




书目名称Applications of Supervised and Unsupervised Ensemble Methods读者反馈




书目名称Applications of Supervised and Unsupervised Ensemble Methods读者反馈学科排名

Initial

Evade Hard Multiple Classifier Systems,uracy. MCSs are currently used in several kinds of applications, among which security applications like biometric identity recognition, intrusion detection in computer networks and spam filtering. However security systems operate in . against intelligent adversaries who try to evade them, and are th

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A Personal Antispam System Based on a Behaviour-Knowledge Space Approach,ll companies and large public or private institutions feel that spam has weakened the reliability and effectiveness of email as an efficient tool for communicating. To establish simple, fast and effective countermeasures against spam attacks is a necessary strategy of a modern mailing management sys

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Restenosis

Partitioner Trees for Classification: A New Ensemble Method, tries to exploit information on each classifier’s area of expertise (Grading, Delegating, Arbitrating). This paper presents a new ensemble method called partitioner trees that combines both approaches. Information on misclassifications is used to train meta classifiers called partitioners and to sp

CHOKE

Disturbing Neighbors Diversity for Decision Forests,r diversity (how different these base classifiers outputs are from each other). An approach for increasing the diversity of the base classifiers is presented in this paper. The method builds some new features to be added to the training dataset of the base classifier. Those new features are computed

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Improving Supervised Learning with Multiple Clusterings,amples are available. When one has only few samples, the obtained model tends to offer poor results. Even when labeled samples are difficult to get, a lot of unlabeled samples are generally available on which unsupervised learning can be done. In this chapter, a way to combine supervised and unsuper

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The Neighbors Voting Algorithm and Its Applications,dimensional data. In the computer vision and image processing fields, this algorithm has been applied to solve various problems like stereo-matching, 3D reconstruction, and image inpainting . In this paper we propose a new technique, inspired to the TVF, that allows to estimate the dimensionality an

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