Machine Learning and Data Mining in Pattern Recognition

Beschreibung

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.

Ever wondered what the state of the art is in machine learning and data mining? Well, now you can find out.

This excellent book constitutes the refereed proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, held in Leipzig, Germany, in July 2007.

The 66 revised full papers presented together with 1 invited talk were carefully reviewed and selected from more than 250 submissions. The papers are organized in topical sections and the package features online content as well as updates.

These sections cover classification; feature selection, extraction and dimensionality reduction; clustering; and support vector machines.

The book also features right up-to-date information on transductive inference; association rule mining; mining spam, newsgroups and blogs; and intrusion detection and networks.

Next, the papers featured cover frequent and common item set mining; mining marketing data; structural data mining; image mining; and medical, biological, and environmental data mining

Lastly, the book covers the highly relevant topic of text and document mining.

Autor*in

Petra Perner

Themen in »Machine Learning and Data Mining in Pattern Recognition«

Spam classification cognition data mining learning machine learning pattern recognition

Stimmen zu »Machine Learning and Data Mining in Pattern Recognition«

Details

Link teilen