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Advanced Lectures on Machine Learning

Olivier Bousquet
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      Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600.This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references.Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.
      Format: Paperback / softback CONTRIBUTORS: Olivier Bousquet EAN: 9783540231226 COUNTRY: Germany PAGES: WEIGHT: 820 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG DATE PUBLISHED: 2004-09-02 CITY: GENRE: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Computer Science, COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition, COMPUTERS / Machine Theory, COMPUTERS / Programming / Algorithms WIDTH: 155 cm SPINE:

      Book Themes:

      Algorithms and data structures, Computer science, Mathematical theory of computation, Artificial intelligence, Pattern recognition

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      Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600.This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references.Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.
      Format: Paperback / softback CONTRIBUTORS: Olivier Bousquet EAN: 9783540231226 COUNTRY: Germany PAGES: WEIGHT: 820 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG DATE PUBLISHED: 2004-09-02 CITY: GENRE: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Computer Science, COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition, COMPUTERS / Machine Theory, COMPUTERS / Programming / Algorithms WIDTH: 155 cm SPINE:

      Book Themes:

      Algorithms and data structures, Computer science, Mathematical theory of computation, Artificial intelligence, Pattern recognition

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      Be the first to write a review
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