Machine Learning and Data Mining in Pattern Recognition -

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Résumé :
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.

Sommaire:
Invited Paper.- Technology of Text Mining.- Evaluation of Clinical Relevance of Clinical Laboratory Investigations by Data Mining.- Case-Based Reasoning and Associative Memory.- Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications.- Are Case-Based Reasoning and Dissimilarity-Based Classification Two Sides of the Same Coin?.- FAM-Based Fuzzy Inference for Detecting Shot Transitions.- Rule Induction and Grammars.- Rule-Based Ensemble Solutions for Regression.- Learning XML Grammars.- First-Order Rule Induction for the Recognition of Morphological Patterns in Topographic Maps.- Clustering and Conceptual Clustering.- Concepts Learning with Fuzzy Clustering and Relevance Feedback.- LC: A Conceptual Clustering Algorithm.- Data Mining on Signal, Images, Text and Temporal-Spatial Data.- Data Mining Approach Based on Information-Statistical Analysis: Application to Temporal-Spatial Data.- A Hybrid Tool for Data Mining in Picture Archiving System.- Feature Selection for a Real-World Learning Task.- Automatic Identification of Diatoms Using Decision Forests.- Validation of Text Clustering Based on Document Contents.- Nonlinear Function Learning and Neural Net Based Learning.- Statistical and Neural Approaches for Estimating Parameters of a Speckle Model Based on the Nakagami Distribution.- How to Automate Neural Net Based Learning.- Nonlinear Function Learning and Classification Using Optimal Radial Basis Function Networks.- Learning for Handwriting Recognition.- Local Learning Framework for Recognition of Lowercase Handwritten Characters.- Mirror Image Learning for Handwritten Numeral Recognition.- Statistical and Evolutionary Learning.- Face Detection by Aggregated Bayesian Network Classifiers.- Towards Self-Exploring DiscriminatingFeatures.- PCA-Based Model Selection and Fitting for Linear Manifolds.- Statistics of Flow Vectors and Its Application to the Voting Method for the Detection of Flow Fields.- On the Use of Pairwise Comparison of Hypotheses in Evolutionary Learning Applied to Learning from Visual Examples.- Featureless Pattern Recognition in an Imaginary Hilbert Space and Its Application to Protein Fold Classification.- Content-Based Image Retrieval.- Adaptive Query Shifting for Content-Based Image Retrieval.- Content-Based Similarity Assessment in Multi-segmented Medical Image Data Bases.