Algebraic Geometry And Statistical Learning Theory
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Algebraic Geometry And Statistical Learning Theory
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Author : Sumio Watanabe
language : en
Publisher: Cambridge University Press
Release Date : 2009-08-13
Algebraic Geometry And Statistical Learning Theory written by Sumio Watanabe and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-13 with Computers categories.
Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geometry and singularity theory provide the necessary tools for studying such non-smooth models. Four main formulas are established: 1. the log likelihood function can be given a common standard form using resolution of singularities, even applied to more complex models; 2. the asymptotic behaviour of the marginal likelihood or 'the evidence' is derived based on zeta function theory; 3. new methods are derived to estimate the generalization errors in Bayes and Gibbs estimations from training errors; 4. the generalization errors of maximum likelihood and a posteriori methods are clarified by empirical process theory on algebraic varieties.
Algebraic Geometry And Statistical Learning Theory
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Author : Sumio Watanabe
language : en
Publisher:
Release Date : 2009
Algebraic Geometry And Statistical Learning Theory written by Sumio Watanabe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computational learning theory categories.
Differential Geometry And Statistics
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Author : M.K. Murray
language : en
Publisher: Routledge
Release Date : 2017-10-19
Differential Geometry And Statistics written by M.K. Murray and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-19 with Mathematics categories.
Several years ago our statistical friends and relations introduced us to the work of Amari and Barndorff-Nielsen on applications of differential geometry to statistics. This book has arisen because we believe that there is a deep relationship between statistics and differential geometry and moreoever that this relationship uses parts of differential geometry, particularly its 'higher-order' aspects not readily accessible to a statistical audience from the existing literature. It is, in part, a long reply to the frequent requests we have had for references on differential geometry! While we have not gone beyond the path-breaking work of Amari and Barndorff- Nielsen in the realm of applications, our book gives some new explanations of their ideas from a first principles point of view as far as geometry is concerned. In particular it seeks to explain why geometry should enter into parametric statistics, and how the theory of asymptotic expansions involves a form of higher-order differential geometry. The first chapter of the book explores exponential families as flat geometries. Indeed the whole notion of using log-likelihoods amounts to exploiting a particular form of flat space known as an affine geometry, in which straight lines and planes make sense, but lengths and angles are absent. We use these geometric ideas to introduce the notion of the second fundamental form of a family whose vanishing characterises precisely the exponential families.
Algebraic Statistics For Computational Biology
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Author : L. Pachter
language : en
Publisher: Cambridge University Press
Release Date : 2005-08-22
Algebraic Statistics For Computational Biology written by L. Pachter and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08-22 with Mathematics categories.
This book, first published in 2005, offers an introduction to the application of algebraic statistics to computational biology.
Geometric Structures Of Statistical Physics Information Geometry And Learning
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Author : Frédéric Barbaresco
language : en
Publisher: Springer Nature
Release Date : 2021-06-27
Geometric Structures Of Statistical Physics Information Geometry And Learning written by Frédéric Barbaresco and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-27 with Mathematics categories.
Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. Conversely, limitations and pitfalls encountered in AI question the very foundations of statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can shed new light on the conceptual basis of AI. During the last fifty years, statistical physics has been investigated through new geometric structures allowing covariant formalization of the thermodynamics. Inference methods in machine learning have begun to adapt these new geometric structures to process data in more abstract representation spaces. This volume collects selected contributions on the interplay of statistical physics and artificial intelligence. The aim is to provide a constructive dialogue around a common foundation to allow the establishment of new principles and laws governing these two disciplines in a unified manner. The contributions were presented at the workshop on the Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning which was held in Les Houches in July 2020. The various theoretical approaches are discussed in the context of potential applications in cognitive systems, machine learning, signal processing.
The Nature Of Statistical Learning Theory
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Author : Vladimir Vapnik
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
The Nature Of Statistical Learning Theory written by Vladimir Vapnik and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Mathematics categories.
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: * the setting of learning problems based on the model of minimizing the risk functional from empirical data * a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its consistency * non-asymptotic bounds for the risk achieved using the empirical risk minimization principle * principles for controlling the generalization ability of learning machines using small sample sizes based on these bounds * the Support Vector methods that control the generalization ability when estimating function using small sample size. The second edition of the book contains three new chapters devoted to further development of the learning theory and SVM techniques. These include: * the theory of direct method of learning based on solving multidimensional integral equations for density, conditional probability, and conditional density estimation * a new inductive principle of learning. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. Vladimir N. Vapnik is Technology Leader AT&T Labs-Research and Professor of London University. He is one of the founders of
Mathematics For Machine Learning
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Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23
Mathematics For Machine Learning written by Marc Peter Deisenroth and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-23 with Computers categories.
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
Understanding Machine Learning
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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19
Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Introduction To Statistical Machine Learning
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Author : Masashi Sugiyama
language : zh-CN
Publisher:
Release Date : 2018
Introduction To Statistical Machine Learning written by Masashi Sugiyama and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Machine learning categories.
Lectures On Algebraic Statistics
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Author : Mathias Drton
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-04-25
Lectures On Algebraic Statistics written by Mathias Drton and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-25 with Mathematics categories.
How does an algebraic geometer studying secant varieties further the understanding of hypothesis tests in statistics? Why would a statistician working on factor analysis raise open problems about determinantal varieties? Connections of this type are at the heart of the new field of "algebraic statistics". In this field, mathematicians and statisticians come together to solve statistical inference problems using concepts from algebraic geometry as well as related computational and combinatorial techniques. The goal of these lectures is to introduce newcomers from the different camps to algebraic statistics. The introduction will be centered around the following three observations: many important statistical models correspond to algebraic or semi-algebraic sets of parameters; the geometry of these parameter spaces determines the behaviour of widely used statistical inference procedures; computational algebraic geometry can be used to study parameter spaces and other features of statistical models.