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Statistical learning theory and stochastic optimization

By: Material type: TextTextLanguage: English Series: Lecture notes in mathematics; v 1851Publication details: Berlin Springer-Verlag 2004Description: viii,272pISBN:
  • 3-540-22572-2
Subject(s): DDC classification:
  • 519.2 Su955
Summary: This book is aimed at analyzing complex data with necessarily approximate models. It is intended for an audience with a graduate background in probability theory & statistics. It will be useful to any reader wondering why it may be a good idea, to use as is often done in practice a notoriously wrong'' (i.e. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, & PAC-Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models & corresponding estimators.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 519.2 Su955 (Browse shelf(Opens below)) Available 125284
Total holds: 0

This book is aimed at analyzing complex data with necessarily approximate models. It is intended for an audience with a graduate background in probability theory & statistics. It will be useful to any reader wondering why it may be a good idea, to use as is often done in practice a notoriously wrong'' (i.e. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, & PAC-Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models & corresponding estimators.

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