Machine Learning and Data Mining in Pattern Recognition

First International Workshop, MLDM'99, Leipzig, Germany, September 16-18, 1999, Proceedings

AvPetra Perner,Maria Petrou

Häftad, Engelska, 1999

Del i serien Lecture Notes in Computer Science

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Beskrivning

This book constitutes the refereed proceedings of the First International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM'99, held in Leipzig, Germany in September 1999. The 15 revised full papers presented together with two invited contributions were carefully reviewed. The papers are organized in sections on neural networks applied to image processing and recognition, learning in image pre-processing and segmentation, image retrieval, classification and image interpretation, symbolic learning and neural networks in document processing, and data mining.

Produktinformation

Utgivningsdatum:1999-09-08
Mått:155 x 235 x 13 mm
Vikt:359 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:224
Förlag:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:9783540665991

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT

Innehållsförteckning

Invited Papers.- Learning in Pattern Recognition.- Advances in Predictive Data Mining Methods.- Neural Networks Applied to Image Processing and Recognition.- Multi-valued and Universal Binary Neurons: Learning Algorithms, Application to Image Processing and Recognition.- A Dynamics of the Hough Transform and Artificial Neural Networks.- Applications of Cellular Neural Networks for Shape from Shading Problem.- Learning in Image Pre-Processing and Segmentation.- Unsupervised Learning of Local Mean Gray Values for Image Pre-processing.- Neural Networks in MR Image Estimation from Sparsely Sampled Scans.- Extraction of Local Structural Features in Images by Using a Multi-scale Relevance Function.- Image Retrieval.- Independent Feature Analysis for Image Retrieval.- Non-hierarchical Clustering with Rival Penalized Competitive Learning for Information Retrieval.- Classification and Image Interpretation.- Automatic Design of Multiple Classifier Systems by Unsupervised Learning.- A Comparison between Neural Networks and Decision Trees.- Symbolic Learning and Neural Networks in Document Processing.- Symbolic Learning Techniques in Paper Document Processing.- Recognition of Printed Music Score.- Data Mining.- Reproductive Process-Oriented Data Mining from Interactions between Human and Complex Artifact System.- Generalised Fuzzy Aggregation Operators.- A Data Mining Application for Monitoring Environmental Risks.