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Second International Workshop, MLDM 2001, Leipzig, Germany, July 25-27, 2001. Proceedings (Lecture Notes in Computer Science)

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The cover of “Machine Learning and Data Mining in Pattern Recognition” - Open Library.

"Machine Learning and Data Mining in Pattern Recognition" is published by Springer in August 24, 2001, it has 363 pages 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:
  • Language: English
  • Number of Pages: 363
  • Publisher: Springer
  • Publish Date:

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Edition Specifications:

  • Format: Paperback
  • Weight: 1.2 pounds
  • Dimensions: 9.1 x 6.1 x 0.9 inches

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"Machine Learning and Data Mining in Pattern Recognition" Description:

The Open Library:

Machine Learning and Data Mining in Pattern Recognition: Second International Workshop, MLDM 2001 Leipzig, Germany, July 25–27, 2001 Proceedings<br />Author: Petra Perner<br /> Published by Springer Berlin Heidelberg<br /> ISBN: 978-3-540-42359-1<br /> DOI: 10.1007/3-540-44596-X<br /><br />Table of Contents:<p></p><ul><li>Technology of Text Mining </li><li>Evaluation of Clinical Relevance of Clinical Laboratory Investigations by Data Mining </li><li>Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications </li><li>Are Case-Based Reasoning and Dissimilarity-Based Classification Two Sides of the Same Coin? </li><li>FAM-Based Fuzzy Inference for Detecting Shot Transitions </li><li>Rule-Based Ensemble Solutions for Regression </li><li>Learning XML Grammars </li><li>First-Order Rule Induction for the Recognition of Morphological Patterns in Topographic Maps </li><li>Concepts Learning with Fuzzy Clustering and Relevance Feedback </li><li>LC: A Conceptual Clustering Algorithm </li><li>Data Mining Approach Based on Information-Statistical Analysis: Application to Temporal-Spatial Data </li><li>A Hybrid Tool for Data Mining in Picture Archiving System </li><li>Feature Selection for a Real-World Learning Task </li><li>Automatic Identification of Diatoms Using Decision Forests </li><li>Validation of Text Clustering Based on Document Contents </li><li>Statistical and Neural Approaches for Estimating Parameters of a Speckle Model Based on the Nakagami Distribution </li><li>How to Automate Neural Net Based Learning </li><li>Nonlinear Function Learning and Classification Using Optimal Radial Basis Function Networks </li><li>Local Learning Framework for Recognition of Lowercase Handwritten Characters </li><li>Mirror Image Learning for Handwritten Numeral Recognition</li></ul>

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