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The Informational Complexity of Learning

Perspectives on Neural Networks and Generative Grammar

  • Book
  • © 1998

Overview

Authors:
  1. Partha Niyogi

    1. Massachusetts Institute of Technology, Cambridge, USA

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xxiii
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  2. Introduction

    • Partha Niyogi
    Pages 1-19

  3. On the Relationship between Hyothesis Complexity, Sample Complexity and Generalization Error for Neural Networks

    • Partha Niyogi
    Pages 21-73

  4. Investigating the Sample Complexity of Active Learning Schemes

    • Partha Niyogi
    Pages 75-123

  5. Language Learning Problems in the Principles and Parameters Framework

    • Partha Niyogi
    Pages 125-171

  6. The Logical Problem of Language Change

    • Partha Niyogi
    Pages 173-205

  7. Conclusions

    • Partha Niyogi
    Pages 207-212

  8. Back Matter

    Pages 213-224
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Keywords

About this book

Among other topics, The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar brings together two important but very different learning problems within the same analytical framework. The first concerns the problem of learning functional mappings using neural networks, followed by learning natural language grammars in the principles and parameters tradition of Chomsky.
These two learning problems are seemingly very different. Neural networks are real-valued, infinite-dimensional, continuous mappings. On the other hand, grammars are boolean-valued, finite-dimensional, discrete (symbolic) mappings. Furthermore the research communities that work in the two areas almost never overlap.
The book's objective is to bridge this gap. It uses the formal techniques developed in statistical learning theory and theoretical computer science over the last decade to analyze both kinds of learning problems. By asking the same question - how much information does it take to learn? - of both problems, it highlights their similarities and differences. Specific results include model selection in neural networks, active learning, language learning and evolutionary models of language change.
The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar is a very interdisciplinary work. Anyone interested in the interaction of computer science and cognitive science should enjoy the book. Researchers in artificial intelligence, neural networks, linguistics, theoretical computer science, and statistics will find it particularly relevant.

Authors and Affiliations

  • Massachusetts Institute of Technology, Cambridge, USA

    Partha Niyogi

Bibliographic Information

  • Book Title: The Informational Complexity of Learning

  • Book Subtitle: Perspectives on Neural Networks and Generative Grammar

  • Authors: Partha Niyogi

  • DOI: https://doi.org/10.1007/978-1-4615-5459-2

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1998

  • Hardcover ISBN: 978-0-7923-8081-8Published: 30 November 1997

  • Softcover ISBN: 978-1-4613-7493-0Published: 16 October 2012

  • eBook ISBN: 978-1-4615-5459-2Published: 06 December 2012

  • Edition Number: 1

  • Number of Pages: XXIII, 224

  • Topics: Artificial Intelligence, Linguistics, general, Theory of Computation, Signal, Image and Speech Processing

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