NEW BOOK by Partha Niyogi

[Federico Girosi]

NEW BOOK *** NEW BOOK *** NEW BOOK *** NEW BOOK *** NEW BOOK *** 

People might be interested in the following book on the relationship
between learning, neural networks and generative grammar.

Federico Girosi


-----------------------------------------------------------------------
The Informational Complexity of Learning: Perspectives on Neural
Networks and Generative Grammar

         Partha Niyogi
   (MIT and Bell Laboratories)

[Kluwer Academic Publishers: ISBN 0792380819]

Among other topics, this book brings together two important but very
different learning problems within the same analytical framework. The
first is the the problem of learning functional mappings using Neural
Networks; the second is learning natural language grammars in the
Principles and Parameters tradition of Chomsky.

The two learning problems are seemingly very different. Neural
networks are real-valued, infinite-dimensional, continuous
mappings. Grammars are boolean-valued, finite-dimensional, discrete
(sympolic) mappings.  Furthermore the research communities that work
in the two areas almost never overlap.

The objective of this book 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. It shows how "setting parameters " in
the principles and parameters tradition of linguistic theory and
"learning the connections" in neural networks are conceptually very
similar problems and both have reasonable statistical formulations. At
the same time, the results from learning theory are used to argue that
both processes must be highly constrained for learning to happen.

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.