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    Multiple Classifier Systems

Multiple Classifier Systems

Jón Atli Benediktsson
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      These proceedings are a record of the Multiple Classi?er Systems Workshop, MCS 2009, held at the University of Iceland, Reykjavik, Iceland in June 2009. Being the eighth in a well-established series of meetings providing an inter- tional forum for the discussion of issues in multiple classi?er system design, the workshop achieved its objective of bringing together researchers from diverse communities (neural networks,pattern recognition,machine learning and stat- tics) concerned with this research topic. From more than 70 submissions, the Program Committee selected 54 papers to create an interesting scienti?c program. The special focus of MCS 2009 was on the application of multiple classi?er systems in remote sensing. This part- ular application uses multiple classi?ers for raw data fusion, feature level fusion and decision level fusion. In addition to the excellent regular submission in the technical program, outstanding contributions were made by invited speakers Melba Crawford from Purdue University and Zhi-Hua Zhou of Nanjing Univ- sity. Papers of these talks are included in these workshop proceedings. With the workshop’sapplicationfocusbeingonremotesensing,Prof.Crawford’sexpertise in the use of multiple classi?cation systems in this context made the discussions on this topic at MCS 2009 particularly fruitful.
      Format: Paperback / softback CONTRIBUTORS: Jón Atli Benediktsson EAN: 9783642023255 COUNTRY: Germany PAGES: WEIGHT: 842 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG DATE PUBLISHED: 2009-06-02 CITY: GENRE: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Software Development & Engineering / Computer Graphics, COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition, COMPUTERS / Programming / General WIDTH: 155 cm SPINE:

      Book Themes:

      Computer programming / software engineering, Mathematical theory of computation, Artificial intelligence, Pattern recognition, Computer vision

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      These proceedings are a record of the Multiple Classi?er Systems Workshop, MCS 2009, held at the University of Iceland, Reykjavik, Iceland in June 2009. Being the eighth in a well-established series of meetings providing an inter- tional forum for the discussion of issues in multiple classi?er system design, the workshop achieved its objective of bringing together researchers from diverse communities (neural networks,pattern recognition,machine learning and stat- tics) concerned with this research topic. From more than 70 submissions, the Program Committee selected 54 papers to create an interesting scienti?c program. The special focus of MCS 2009 was on the application of multiple classi?er systems in remote sensing. This part- ular application uses multiple classi?ers for raw data fusion, feature level fusion and decision level fusion. In addition to the excellent regular submission in the technical program, outstanding contributions were made by invited speakers Melba Crawford from Purdue University and Zhi-Hua Zhou of Nanjing Univ- sity. Papers of these talks are included in these workshop proceedings. With the workshop’sapplicationfocusbeingonremotesensing,Prof.Crawford’sexpertise in the use of multiple classi?cation systems in this context made the discussions on this topic at MCS 2009 particularly fruitful.
      Format: Paperback / softback CONTRIBUTORS: Jón Atli Benediktsson EAN: 9783642023255 COUNTRY: Germany PAGES: WEIGHT: 842 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG DATE PUBLISHED: 2009-06-02 CITY: GENRE: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Software Development & Engineering / Computer Graphics, COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition, COMPUTERS / Programming / General WIDTH: 155 cm SPINE:

      Book Themes:

      Computer programming / software engineering, Mathematical theory of computation, Artificial intelligence, Pattern recognition, Computer vision

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