Edited by Stéphane Boucheron and Nicolas Vayatis.
| Softcover ISBN: | 978-2-85629-964-7 |
| Product Code: | PASY/57 |
| List Price: | $57.00 |
| AMS Member Price: | $45.60 |
Edited by Stéphane Boucheron and Nicolas Vayatis.
| Softcover ISBN: | 978-2-85629-964-7 |
| Product Code: | PASY/57 |
| List Price: | $57.00 |
| AMS Member Price: | $45.60 |
-
Book DetailsPanoramas et SynthèsesVolume: 57; 2022; 88 ppMSC: 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.
ReadershipUndergraduate and graduate students interested in computational learning theory, statistical learning theory, empirical processes, clustering, active learning, online learning, and nonparametric regression.
-
Additional Material
-
RequestsReview Copy – for publishers of book reviewsAccessibility – to request an alternate format of an AMS title
- Book Details
- Additional Material
- Requests
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.
Undergraduate and graduate students interested in computational learning theory, statistical learning theory, empirical processes, clustering, active learning, online learning, and nonparametric regression.
