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Machine Learning: The Art and Science of Algorithms that Make Sense of Data, 2012, 396 pages, Peter Flach, 1107096391, 9781107096394, Cambridge University Press, 2012 DOWNLOAD http://bit.ly/1BUumnF http://en.wikipedia.org/wiki/Machine_Learning_The_Art_and_Science_of_Algorithms_that_Make_Sense_of_Data As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the- art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook. DOWNLOAD http://is.gd/40JFqV http://kickass.to/Machine-Learning-The-Art-and-Science-of-Algorithms-that-Make-Sense-of-Data-t2696246.html http://bit.ly/1mDsrQl Text Mining Predictive Methods for Analyzing Unstructured Information, Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred Damerau, Jan 8, 2010, Computers, 248 pages. The growth of the web can be seen as an expanding public digital library collection. Online digital information extends far beyond the web and its publicly available. Multiple Classifier Systems: Third International Workshop, MCS, Volume 3 Third International Workshop, MCS 2002, Cagliari, Italy, June 24-26, 2002. Proceedings, Fabio Roli, Josef Kittler, Jun 12, 2002, Computers, 335 pages. Morethanadecadeago,combiningmultipleclassi?erswasproposedasap- siblesolutiontotheproblemsposedbythetraditionalpatternclassi?cation approachwhichinvolvedselectingthebestclassi. Machine Learning: ECML 2004: 15th European Conference on Machine, Volume 15 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings, Jean-Francois Boulicaut, Sep 7, 2004, Computers, 580 pages. The proceedings of ECML/PKDD 2004 are published in two separate, albeit - tertwined,volumes:theProceedingsofthe 15thEuropeanConferenceonMac- ne Learning (LNAI 3201) and the. Logical and Relational Learning , Luc De Raedt, Sep 27, 2008, Artificial Intelligence (incl. Robotics), 404 pages. This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and. Machine Learning A Probabilistic Perspective, Kevin P. Murphy, Aug 24, 2012, Computers, 1067 pages. A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.. Models for Object Detection, Recognition, and Shape Alignment , Yan Li, 2009, , 177 pages. The grand goal of computer vision is to provide a complete semantic interpretation of an input image by reasoning about the 3d scene that generated it. Object detection.

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Machine Learning: The Art and Science of Algorithms that Make Sense of Data,2012, 396 pages, Peter Flach, 1107096391, 9781107096394, Cambridge UniversityPress, 2012

DOWNLOAD http://bit.ly/1BUumnF http://en.wikipedia.org/wiki/Machine_Learning_The_Art_and_Science_of_Algorithms_that_Make_Sense_of_Data

As one of the most comprehensive machine learning texts around, this book does justice to thefield's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear,example-based approach begins by discussing how a spam filter works, which gives an immediateintroduction to machine learning in action, with a minimum of technical fuss. Flach provides casestudies of increasing complexity and variety with well-chosen examples and illustrationsthroughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the centralrole played by features. The use of established terminology is balanced with the introduction ofnew and useful concepts, and summaries of relevant background material are provided withpointers for revision if necessary. These features ensure Machine Learning will set a new standardas an introductory textbook.

DOWNLOAD http://is.gd/40JFqV http://kickass.to/Machine-Learning-The-Art-and-Science-of-Algorithms-that-Make-Sense-of-Data-t2696246.html

http://bit.ly/1mDsrQl

Text Mining Predictive Methods for Analyzing Unstructured Information, Sholom M. Weiss, NitinIndurkhya, Tong Zhang, Fred Damerau, Jan 8, 2010, Computers, 248 pages. The growth of theweb can be seen as an expanding public digital library collection. Online digital information extendsfar beyond the web and its publicly available.

Multiple Classifier Systems: Third International Workshop, MCS, Volume 3 Third InternationalWorkshop, MCS 2002, Cagliari, Italy, June 24-26, 2002. Proceedings, Fabio Roli, Josef Kittler, Jun12, 2002, Computers, 335 pages.Morethanadecadeago,combiningmultipleclassi?erswasproposedasap-siblesolutiontotheproblemsposedbythetraditionalpatternclassi?cationapproachwhichinvolvedselectingthebestclassi.

Machine Learning: ECML 2004: 15th European Conference on Machine, Volume 15 15th EuropeanConference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings, Jean-FrancoisBoulicaut, Sep 7, 2004, Computers, 580 pages. The proceedings of ECML/PKDD 2004 are publishedin two separate, albeit - tertwined,volumes:theProceedingsofthe 15thEuropeanConferenceonMac-ne Learning (LNAI 3201) and the.

Logical and Relational Learning , Luc De Raedt, Sep 27, 2008, Artificial Intelligence (incl. Robotics),404 pages. This textbook covers logical and relational learning in depth, and hence provides anintroduction to inductive logic programming (ILP), multirelational data mining (MRDM) and.

Machine Learning A Probabilistic Perspective, Kevin P. Murphy, Aug 24, 2012, Computers, 1067pages. A comprehensive introduction to machine learning that uses probabilistic models andinference as a unifying approach..

Models for Object Detection, Recognition, and Shape Alignment , Yan Li, 2009, , 177 pages. Thegrand goal of computer vision is to provide a complete semantic interpretation of an input imageby reasoning about the 3d scene that generated it. Object detection.

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