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Titlebook: Data Analysis, Machine Learning and Applications; Proceedings of the 3 Christine Preisach,Hans Burkhardt,Reinhold Decker Conference proceed

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书目名称Data Analysis, Machine Learning and Applications
副标题Proceedings of the 3
编辑Christine Preisach,Hans Burkhardt,Reinhold Decker
视频video
概述Includes supplementary material:
丛书名称Studies in Classification, Data Analysis, and Knowledge Organization
图书封面Titlebook: Data Analysis, Machine Learning and Applications; Proceedings of the 3 Christine Preisach,Hans Burkhardt,Reinhold Decker Conference proceed
描述.Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007..
出版日期Conference proceedings 2008
关键词artificial intelligence; bioinformatics; business intelligence; classification; clustering; computer scie
版次1
doihttps://doi.org/10.1007/978-3-540-78246-9
isbn_softcover978-3-540-78239-1
isbn_ebook978-3-540-78246-9Series ISSN 1431-8814 Series E-ISSN 2198-3321
issn_series 1431-8814
copyrightSpringer-Verlag Berlin Heidelberg 2008
The information of publication is updating

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Xiao Yu,Nicholas P. Franks,William Wisdenembeds a dendrogram as a subtree into the Bruhat-Tits tree associated to the .-adic numbers, and goes back to Cornelissen et al. (2001) in .-adic geometry. After explaining the definitions, the concept of classifiers is discussed in the context of moduli spaces, and upper bounds for the number of hidden vertices in dendrograms are given.
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Computer Assisted Classification of Brain Tumorsrocytomas in our database. Furthermore, we discuss the stability of the procedure with respect to errors and propose a different parametrization of the metric in the SRNG algorithm to avoid the introduction of unnecessary boundaries in the parameter space.
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Mixture Models in Forward Search Methods for Outlier Detections as a null model. The proposal is developed according to both the graphical and the inferential approach to FS-based outlier detection. The performance of the method is shown on an illustrative example and evaluated on a simulation experiment in the multiple cluster setting.
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