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Data Analysis, Machine Learning and Knowledge Discovery 2014 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 470 pages, kõrgus x laius: 235x155 mm, kaal: 7372 g, 32 Illustrations, color; 88 Illustrations, black and white; XXI, 470 p. 120 illus., 32 illus. in color., 1 Paperback / softback
  • Sari: Studies in Classification, Data Analysis, and Knowledge Organization
  • Ilmumisaeg: 09-Dec-2013
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 331901594X
  • ISBN-13: 9783319015941
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Data Analysis, Machine Learning and Knowledge Discovery 2014 ed.Suurem pilt
  • Formaat: Paperback / softback, 470 pages, kõrgus x laius: 235x155 mm, kaal: 7372 g, 32 Illustrations, color; 88 Illustrations, black and white; XXI, 470 p. 120 illus., 32 illus. in color., 1 Paperback / softback
  • Sari: Studies in Classification, Data Analysis, and Knowledge Organization
  • Ilmumisaeg: 09-Dec-2013
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 331901594X
  • ISBN-13: 9783319015941
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319015941.html
Märksõnad:
Data analysis, machine learning and knowledge discovery 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, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012. ?

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From the book reviews:

The book is organized in seven parts . The book is a very interesting collection of papers describing various approaches of data mining and machine learning on aspects from bioinformatics to music classification. It is an excellent addition to the field and it can be used as starting point for projects from undergraduate to post-graduate level. (Irina Ioana Mohorianu, zbMATH, Vol. 1301, 2015)

AREA Statistics and Data Analysis: Classifcation, Cluster Analysis,
Factor Analysis and Model Selection.- AREA Machine Learning and Knowledge
Discovery: Clustering, Classifiers, Streams and Social Networks.- AREA Data
Analysis and Classification in Marketing.- AREA Data Analysis in Finance.-
AREA Data Analysis in Biostatistics and Bioinformatics.- AREA
Interdisciplinary Domains: Data Analysis in Music, Education and
Psychology.- LIS Workshop: Workshop on Classification and Subject Indexing in
Library and Information Science.