Titlebook: Supervised and Unsupervised Ensemble Methods and their Applications; Oleg Okun,Giorgio Valentini Book 2008 Springer-Verlag Berlin Heidelbe

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




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




书目名称Supervised and Unsupervised Ensemble Methods and their Applications网络公开度




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




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




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




书目名称Supervised and Unsupervised Ensemble Methods and their Applications年度引用




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




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




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

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Random Subspace Ensembles for Clustering Categorical Datat and stable solutions by leveraging the consensus across multiple clustering results, while averaging out spurious structures that arise due to the various biases to which each participating algorithm is tuned. In this chapter we focus on the design of ensembles for categorical data. Our techniques

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Collaborative Multi-Strategical Clustering for Object-Oriented Image Analysist kinds of clustering algorithms and produces, for each algorithm, a result built according to the results of all the other methods: each method tries to make its result to converge towards the results of the other methods by using consensus operators. This chapter highlights how clustering methods

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Gradient Boosting GARCH and Neural Networks for Time Series Predictioneries. The main novel elements of these new algorithms are as follows: a) in regard to neural networks, the simultaneous estimation of conditional mean and volatility through the likelihood maximization; b) in regard to boosting, its simultaneous application to trend and volatility components of the

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Cascading with VDM and Binary Decision Trees for Nominal Dataethods on nominal data. A different classifier was used at each level. The classifier at the base level transforms the nominal inputs into continuous probabilities that the classifier at the meta level uses as inputs. An experimental validation is provided over 27 nominal datasets for enhancing a me