Machine Learning and Data Mining in Pattern Recognition - Info and Reading Options
6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009. Proceedings
By Petra Perner
"Machine Learning and Data Mining in Pattern Recognition" was published by Springer Berlin Heidelberg in 2009 - Berlin, Heidelberg and the language of the book is English.
“Machine Learning and Data Mining in Pattern Recognition” Metadata:
- Title: ➤ Machine Learning and Data Mining in Pattern Recognition
- Author: Petra Perner
- Language: English
- Publisher: Springer Berlin Heidelberg
- Publish Date: 2009
- Publish Location: Berlin, Heidelberg
“Machine Learning and Data Mining in Pattern Recognition” Subjects and Themes:
- Subjects: ➤ Computer vision - Computer science - Database management - Optical pattern recognition - Data mining - Artificial intelligence - Pattern perception - Congresses - Machine learning - Image processing - Automatische Klassifikation - Kongress - Bildbanksystem - Mustererkennung - Data Mining - Bildverarbeitung - Maschinelles Lernen - Cluster-Analyse - Visuelles Datenbanksystem - Pattern recognition systems - Cluster analysis
Edition Specifications:
- Format: [electronic resource] :
Edition Identifiers:
- The Open Library ID: OL25544937M - OL16940014W
- Online Computer Library Center (OCLC) ID: 427321456
- Library of Congress Control Number (LCCN): 2009930146
- ISBN-13: 9783642030697 - 9783642030703
- All ISBNs: 9783642030697 - 9783642030703
AI-generated Review of “Machine Learning and Data Mining in Pattern Recognition”:
"Machine Learning and Data Mining in Pattern Recognition" Description:
Open Data:
This book constitutes the refereed proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2009, held in Leipzig, Germany, in July 2009. The 63 revised full papers presented were carefully reviewed and selected from 205 submissions. The papers are organized in topical sections on attribute discretization and data preparation; classification; ensemble classifier learning; associate rules and pattern minig; support vector machines; clustering; novelty and outlier detection; learning; data mining and multimedia data; text mining; aspects of data mining; as well as data mining in medicine
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