Algebraic Geometry and Statistical Learning Theory Cambridge Monographs on Applied and Computational Mathematics, Series Number 25 1st Edition
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Algebraic Geometry and Statistical Learning Theory Cambridge Monographs on Applied and Computational Mathematics, Series Number 25 1st Edition
Watanabe’s book lays the foundations for the use of algebraic geometry in statistical learning theory.
Algebraic Geometry and Statistical Learning Theory Cambridge Monographs on Applied and Computational Mathematics, Series Number 25 1st Edition
Numéro d'article: 58626472

Algebraic Geometry and Statistical Learning Theory Cambridge Monographs on Applied and Computational Mathematics, Series Number 25 1st Edition

Numéro d'article: 58626472

MGA 489931

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Watanabe’s book lays the foundations for the use of algebraic geometry in statistical learning theory.
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Ce qui se démarque

Interdisciplinary Approach
Combines algebraic geometry and statistical learning theory, offering a unique perspective that bridges mathematics and statistics for advanced learners and researchers.
Comprehensive Coverage
Provides in-depth theoretical insights and practical applications, making complex concepts accessible for graduate students and professionals in applied mathematics.
Renowned Authors
Authored by leading experts in the field, ensuring high-quality content that reflects current trends and research in applied and computational mathematics.

Détails du produit

Shop Algebraic Geometry and Statistical Learning Theory Cambridge Monographs on Applied and Computational Mathematics, Series Number 25 1st Edition online at a best price in Madagascar. 0521864674
  • Foundational book by Watanabe integrating algebraic geometry into statistical learning theory
  • Addresses singular models/machines such as mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars
  • Provides theoretical basis for accurate estimation techniques in the presence of singularities
  • Part of the Cambridge Monographs on Applied and Computational Mathematics series
  • Sure to have a significant impact in the field of statistical learning theory
  • First edition of the book
Publisher Cambridge University Press
Publication date September 28, 2009
Edition 1st
Language English
Print length 300 pages
ISBN-10 0521864674
ISBN-13 978-0521864671
Item Weight 1.25 pounds (570 grams)
Dimensions 6.25 x 1 x 9 inches (15.9 x 2.5 x 22.9 cm)

À qui est-ce destiné ?

Suitable For
  • Graduate Students

    Ideal for advanced graduate students focusing on the intersection of algebraic geometry and statistical learning theory.

  • Research Professionals

    Useful for researchers seeking to apply algebraic geometry concepts in statistical learning frameworks and computational mathematics.

  • Mathematical Statisticians

    Perfect for statisticians interested in theoretical foundations and mathematical underpinnings of statistical learning algorithms.

Not Suitable For
  • Casual Readers

    Not suitable for casual readers; the content is complex and requires foundational knowledge in mathematics.

  • Undergraduate Students

    Undergraduates may find the material too advanced without prior exposure to algebraic geometry or statistical theory.

  • Practitioners Only

    This book is theoretical and may not cater to practitioners seeking practical applications without mathematical rigor.

DESCRIPTION DU PRODUIT

Algebraic Geometry and Statistical Learning Theory Cambridge Monographs on Applied and Computational Mathematics, Series Number 25 1st Edition

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Questions et réponses des clients

  • question: What is the focus of Watanabe's book?

    répondre: The book lays the foundations for the use of algebraic geometry in statistical learning theory.
  • question: What are some examples of singular models/machines?

    répondre: Mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are some of the major examples.
  • question: What is the usefulness of this book?

    répondre: The theory achieved in the book will help in accurate estimation techniques in the presence of singularities.

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Caractéristiques et avantages

  • Algebraic geometry can now be used in statistical learning theory
  • The book focuses on singular models/machines
  • Major examples of such models include mixture models, neural networks, HMMs, Bayesian networks etc.
  • The theory lays the groundwork for accurate estimation in the presence of singularities.

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