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Topics in Statistical Learning Theory
 
Peter L. Bartlett Berkeley AI Research Laboratory, University of California, Berkeley, CA
Sanjoy Dasgupta University of California, San Diego

Edited by Stéphane Boucheron and Nicolas Vayatis.

A publication of the Société Mathématique de France
Topics in Statistical Learning Theory
Softcover ISBN:  978-2-85629-964-7
Product Code:  PASY/57
List Price: $57.00
AMS Member Price: $45.60
Please note AMS points can not be used for this product
Topics in Statistical Learning Theory
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Topics in Statistical Learning Theory
Peter L. Bartlett Berkeley AI Research Laboratory, University of California, Berkeley, CA
Sanjoy Dasgupta University of California, San Diego

Edited by Stéphane Boucheron and Nicolas Vayatis.

A publication of the Société Mathématique de France
Softcover ISBN:  978-2-85629-964-7
Product Code:  PASY/57
List Price: $57.00
AMS Member Price: $45.60
Please note AMS points can not be used for this product
  • Book Details
     
     
    Panoramas et Synthèses
    Volume: 572022; 88 pp
    MSC: Primary 68; 62; 60;

    This volume is the outcome of a series of three lectures on statistical learning theory given at Institut Henri Poincaré in 2011 under the auspices of the Société Mathéatique de France. The introductory chapter provides an overview of the history of Statistical Learning Theory, its roots, and its mathematical tools. The chapter Algorithms for minimally supervised learning, by Sanjoy Dasgupta, describes the progress of theoretical computer science on the issues of unsupervised learning (clustering) and active learning. Surprisingly, much of this progress is due to the confrontation of measurement concentration theory, complexity theory, and established practices in numerical statistics.

    The chapter Online prediction, by Peter Bartlett, focuses on online learning. It is a confrontation between statistics, game theory and optimization.

    A publication of the Société Mathématique de France, Marseilles (SMF), distributed by the AMS in the U.S., Canada, and Mexico. Orders from other countries should be sent to the SMF. Members of the SMF receive a 30% discount from list.

    Readership

    Undergraduate and graduate students interested in computational learning theory, statistical learning theory, empirical processes, clustering, active learning, online learning, and nonparametric regression.

  • Additional Material
     
     
  • Requests
     
     
    Review Copy – for publishers of book reviews
    Accessibility – to request an alternate format of an AMS title
Volume: 572022; 88 pp
MSC: Primary 68; 62; 60;

This volume is the outcome of a series of three lectures on statistical learning theory given at Institut Henri Poincaré in 2011 under the auspices of the Société Mathéatique de France. The introductory chapter provides an overview of the history of Statistical Learning Theory, its roots, and its mathematical tools. The chapter Algorithms for minimally supervised learning, by Sanjoy Dasgupta, describes the progress of theoretical computer science on the issues of unsupervised learning (clustering) and active learning. Surprisingly, much of this progress is due to the confrontation of measurement concentration theory, complexity theory, and established practices in numerical statistics.

The chapter Online prediction, by Peter Bartlett, focuses on online learning. It is a confrontation between statistics, game theory and optimization.

A publication of the Société Mathématique de France, Marseilles (SMF), distributed by the AMS in the U.S., Canada, and Mexico. Orders from other countries should be sent to the SMF. Members of the SMF receive a 30% discount from list.

Readership

Undergraduate and graduate students interested in computational learning theory, statistical learning theory, empirical processes, clustering, active learning, online learning, and nonparametric regression.

Review Copy – for publishers of book reviews
Accessibility – to request an alternate format of an AMS title
Please select which format for which you are requesting permissions.