Machine Learning and Data Mining in Pattern Recognition - Info and Reading Options
Second International Workshop, MLDM 2001, Leipzig, Germany, July 25-27, 2001. Proceedings
By Petra Perner and Atsushi Imiya
"Machine Learning and Data Mining in Pattern Recognition" was published by Springer London, Limited in 2006 - 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
- Authors: Petra PernerAtsushi Imiya
- Language: English
- Publisher: Springer London, Limited
- Publish Date: 2006
- 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 - Information storage and retrieval systems - Artificial intelligence - Pattern perception - Machine learning - Data mining - Image processing - Pattern recognition systems - Congresses - Artificial Intelligence (incl. Robotics) - Computer software - Data Mining and Knowledge Discovery - Algorithm Analysis and Problem Complexity - Nonfiction
Edition Specifications:
- Pagination: 363
Edition Identifiers:
- The Open Library ID: OL37089892M - OL16931721W
- ISBN-13: 9783540445968 - 9783540423591
- All ISBNs: 9783540445968 - 9783540423591
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 Second International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2001, held in Leipzig, Germany in July 2001. The 26 revised full papers presented together with two invited papers were carefully reviewed and selected for inclusion in the proceedings. The papers are organized in topical sections on case-based reasoning and associative memory; rule induction and grammars; clustering and conceptual clustering; data mining on signals, images, and spatio-temporal data; nonlinear function learning and neural net based learning; learning for handwriting recognition; statistical and evolutionary learning; and content-based image retrieval
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