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Can I read Machine Learning and Data Mining in Pattern Recognition: Third International Conference, MLDM 2003, Leipzig, Germany, July 5-7, 2003, proceedings (Lecture Notes in Computer Science, 2734) on EtoBox?
Machine Learning and Data Mining in Pattern Recognition: Third International Conference, MLDM 2003, Leipzig, Germany, July 5-7, 2003, proceedings (Lecture Notes in Computer Science, 2734) by Petra Perner, Azriel Rosenfeld is a nonfiction available to read on EtoBox.
What is Machine Learning and Data Mining in Pattern Recognition: Third International Conference, MLDM 2003, Leipzig, Germany, July 5-7, 2003, proceedings (Lecture Notes in Computer Science, 2734) about?
TheInternationalConferenceonMachineLearningandDataMining(MLDM)is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. MLDM began as a workshop and is now a conference, and has brought the topic of machine learning and data mining to the attention of the research community. Seventy-?ve papers were submitted to the conference this year. The program committeeworkedhardtoselectthemostprogressiveresearchinafairandc- petent review process which led to the acceptance of 33 papers for presentation at the conference. The 33 papers in these proceedings cover a wide variety of topics related to machine learning and data mining. The two invited talks deal with learning in case-based reasoning and with mining for structural data. The contributed papers can be grouped into nine areas: support vector machines; pattern dis- very; decision trees; clustering; classi?cation and retrieval; case-based reasoning; Bayesian models and methods; association rules; and applications. We would like to express our appreciation to the reviewers for their precise andhighlyprofessionalwork.Wearegratef
Who reads Machine Learning and Data Mining in Pattern Recognition: Third International Conference, MLDM 2003, Leipzig, Germany, July 5-7, 2003, proceedings (Lecture Notes in Computer Science, 2734)?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Petra Perner, Azriel Rosenfeld
- Publisher
- Springer Science & Business Media
- Published
- 2003
- Language
- EN
- ISBN
- 9783540450658
- Category
- nonfiction
- Subjects
- Science, Computer Science, Management
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