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Home > Classification - the Ubiquitous Challenge: Proceedings of the 28th Annual Conference of the Gesellschaft für Klassifikation e. V. , University of Dortmund, March 9-11, 2004 > Classification and Data Mining in Musicology
Classification - the Ubiquitous Challenge: Proceedings of the 28th Annual Conference of the Gesellschaft für Klassifikation e. V. , University of Dortmund, March 9-11, 2004 |
Claus Weihs; Wolfgang A. Gaul
Springer Berlin Heidelberg
Classification - the Ubiquitous Challenge: Proceedings of the 28th Annual Conference of the Gesellschaft für Klassifikation e. V. , University of Dortmund, March 9-11, 2004
Classification and Data Mining in Musicology
Authors

Jan Beran

ARI Id

1665029141922_4532472

Access

Not Available Free

Pages

3-10

DOI

10.1007/3-540-28084-7_1

Chapter URL

https://rd.springer.com/chapter/10.1007/3-540-28084-7_1

Table of Contents of Book
Showing 1 to 20 of 84 entries
Chapters/HeadingsAuthor(s)PagesInfo
Front Matter
I-XX
Classification and Data Mining in Musicology
Jan Beran
3-10
Bayesian Mixed Membership Models for Soft Clustering and Classification
Elena A. Erosheva; Stephen E. Fienberg
11-26
Predicting Protein Secondary Structure with Markov Models
Paul Fischer; Simon Larsen; Claus Thomsen
27-33
Milestones in the History of Data Visualization: A Case Study in Statistical Historiography
Michael Friendly
34-52
Quantitative Text Typology: The Impact of Word Length
Peter Grzybek; Ernst Stadlober; Emmerich Kelih; Gordana Antić
53-64
Cluster Ensembles
Kurt Hornik
65-72
Bootstrap Confidence Intervals for Three-way Component Methods
Henk A.L. Kiers
73-84
Organising the Knowledge Space for Software Components
Claus Pahl
85-96
Multimedia Pattern Recognition in Soccer Video Using Time Intervals
Cees G.M. Snoek; Marcel Worring
97-108
Quantitative Assessment of the Responsibility for the Disease Load in a Population
Wolfgang Uter; Olaf Gefeller
109-117
Bootstrapping Latent Class Models
José G. Dias
121-128
Dimensionality of Random Subspaces
Eugeniusz Gatnar
129-136
Two-stage Classification with Automatic Feature Selection for an Industrial Application
Sören Hader; Fred A. Hamprecht
137-144
Bagging, Boosting and Ordinal Classification
Klaus Hechenbichler; Gerhard Tutz
145-152
A Method for Visual Cluster Validation
Christian Hennig
153-160
Empirical Comparison of Boosting Algorithms
Riadh Khanchel; Mohamed Limam
161-167
Iterative Majorization Approach to the Distance-based Discriminant Analysis
Serhiy Kosinov; Stéphane Marchand-Maillet; Thierry Pun
168-175
An Extension of the CHAID Tree-based Segmentation Algorithm to Multiple Dependent Variables
Jay Magidson; Jeroen K. Vermunt
176-183
Expectation of Random Sets and the ‘Mean Values’ of Interval Data
Ole Nordhoff
184-191
Chapters/HeadingsAuthor(s)PagesInfo
Showing 1 to 20 of 84 entries
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