Machine Learning and Data Mining in Pattern Recognition - Info and Reading Options
By Petra Perner

“Machine Learning and Data Mining in Pattern Recognition” Metadata:
- Title: ➤ Machine Learning and Data Mining in Pattern Recognition
- Author: Petra Perner
“Machine Learning and Data Mining in Pattern Recognition” Subjects and Themes:
- Subjects: Pattern perception - Machine learning - Congresses - Image processing - Data mining
Edition Identifiers:
- The Open Library ID: OL9074345W
AI-generated Review of “Machine Learning and Data Mining in Pattern Recognition”:
"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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