Please use this identifier to cite or link to this item: https://lib.hpu.edu.vn/handle/123456789/32784
Title: Machine Learning and Data Mining in Pattern Recognition: 5th International Conference, MLDM 2007, Leipzig, Germany, July 18-20, 2007. Proceedings
Authors: Jain, Anil K.
Perner, Petra
Keywords: Artificial Intelligence
Mathematical Logic and Formal Languages
Database Management
Data Mining and Knowledge Discovery
Pattern Recognition
Image Processing and Computer Vision
Issue Date: 2007
Publisher: Springer-Verlag Berlin Heidelberg
Abstract: MLDM / ICDM Medaillie Meissner Porcellan, the “White Gold” of King August the Strongest of Saxonia Gottfried Wilhelm von Leibniz, the great mathematician and son of Leipzig, was watching over us during our event in Machine Learning and Data Mining in Pattern Recognition (MLDM 2007). He can be proud of what we have achieved in this area so far. We had a great research program this year. This was the fifth MLDM in Pattern Recognition event held in Leipzig (www.mldm.de). Today, there are many international meetings carrying the title machine learning and data mining, whose topics are text mining, knowledge discovery, and applications. This meeting from the very first event has focused on aspects of machine learning and data mining in pattern recognition problems. We planned to reorganize classical and well-established pattern recognition paradigms from the view points of machine learning and data mining. Although it was a challenging program in the late 1990s, the idea has provided new starting points in pattern recognition and has influenced other areas such as cognitive computer vision. For this edition, the Program Committee received 258 submissions from 37 countries (see Fig. 1). To handle this high number of papers was a big challenge for the reviewers. Every paper was thoroughly reviewed and all authors received a detailed report on their submitted work.
URI: https://lib.hpu.edu.vn/handle/123456789/32784
ISBN: 3540734988
9783540734987
Appears in Collections:Technology

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