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1.

電子ブック

EB
edited by Salvatore Ingrassia, Roberto Rocci, Maurizio Vichi
出版情報: Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg, 2011
シリーズ名: Studies in Classification, Data Analysis, and Knowledge Organization ;
オンライン: http://dx.doi.org/10.1007/978-3-642-11363-5
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2.

電子ブック

EB
edited by Paolo Giudici, Salvatore Ingrassia, Maurizio Vichi
出版情報: Heidelberg : Springer International Publishing : Imprint: Springer, 2013
シリーズ名: Studies in Classification, Data Analysis, and Knowledge Organization ;
オンライン: http://dx.doi.org/10.1007/978-3-319-00032-9
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目次情報: 続きを見る
From the contents: Ordering curves by data depth
May the students' career performance helpful in predicting an increase in universities income?
Model-based classification via patterned covariance analysis
Data stream summarization by histograms clustering
Web panel representativeness
Nonparametric inference via permutation tests for CUB models
Asymmetric multidimensional scaling models for seriations
An approach to ranking the hedge funds industry
Correction of incoherences in statistical matching
The analysis of network additionality in the context of territorial innovation policy
Clustering and registration of multidimensional functional data
Classifying tourism destinations
On two classes of weighted rank correlation measures deriving from teh Spearman's r
Beanplot data analysis in a temporal framework
Supervised classification of facial expressions
Grouping around different dimensional affine subspaces
Hospital clustering in the treatment of acute myocardial infarction patients via a Bayesian semiparametric approach
A new fuzzy method to classify professional profiles from job announcements
A metric based approach for the Least Square Regression of multivariate model symbolic data
A Gaussian-Von Mises Hidden Markov model for clustering multivariate linear-circular data
And further articles.
From the contents: Ordering curves by data depth
May the students' career performance helpful in predicting an increase in universities income?
Model-based classification via patterned covariance analysis