Machine Learning and Data Mining in Pattern Recognition: 4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005, Proceedings . / Lecture Notes in Artificial Intelligence)

Perner, Petra [Editor]; Imiya, Atsushi [Editor];

ISBN 10: 3540269231 / ISBN 13: 9783540269236

Edité par Springer, 2005

A propos de cet article

Ex-library paperback in very nice condition with the usual markings and attachments.

N° de réf. du vendeur UTD14a17923

Détails bibliographiques

• Titre: Machine Learning and Data Mining in Pattern Recognition: 4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005, Proceedings . / Lecture Notes in Artificial Intelligence)
• Éditeur: Springer
• Date de parution: 2005
• Langue: anglais
• ISBN à 10 chiffres: 3540269231
• ISBN à 13 chiffres: 9783540269236
• Reliure: Paperback
• État de l'article: Very Good

A propos de ce titre

Synopsis

We met again in front of the statue of Gottfried Wilhelm von Leibniz in the city of Leipzig. Leibniz, a famous son of Leipzig, planned automatic logical inference using symbolic computation, aimed to collate all human knowledge. Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition such as bioinformatics and data retrieval. This was the fourth edition of MLDM in Pattern Recognition which is the main event of Technical Committee 17 of the International Association for Pattern Recognition; it started out as a workshop and continued as a conference in 2003. Today, there are many international meetings which are titled "machine learning" and "data mining", whose topics are text mining, knowledge discovery, and applications. This meeting from the first 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 viewpoints of machine learning and data mining. Though it was a challenging program in the late 1990s, the idea has inspired new starting points in pattern recognition and effects in other areas such as cognitive computer vision.