Book announcement

[David Wolpert]

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                        *** BOOK ANNOUNCEMENT ***

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TITLE: The Mathematics of Generalization: Proceedings of the SFI/CNLS
Workshop on Formal Approaches to Supervised Learning


Edited by D. Wolpert


Other (first) authors: 
	L. Breiman
	P. Cheeseman
	J. Denker
	T. Dietterich
	D. Haussler
	G. Hinton
	S. Nowlan
	N. Tishby
	G. Wahba

=======================================================================

Table of Contents:



Reflections After Referring Papers for NIPS
        Leo Breiman

The Probably Approximately Correct (PAC) and Other Learning Models
        David Haussler and Manfred Warmuth

Decision Theoretic Generalizations of the PAC Model for Neural Net and
Other Learning Applications
        David Haussler

The Relationshop Between PAC, the Statistical Physics Framework, the
Bayesian Framework, and the VC Framework {a heavily revised version of
a paper that was posted to connectionist.net about six months ago}
        David H. Wolpert

Statistical Physics Models of Supervised Learning
        Naftali Tishby

On Exhaustive Learning
        David H. Wolpert and Alan S. Lapedes

A Study of Maximal-Coverage Learning Algorithms
        Hussein Almuallim and Tom Dietterich

On Bayesian Model Selection
        Peter Cheeseman

Soft Classification, a.k.a. Risk Estimation, via Penalized Log Likelihood
and Smoothing Spline Analysis of Variance
        Grace Wahba, Chong Gu, Yuedong Wang, and Richard Chappell

Current Research
        Leo Brieman

Preface to Simplifying Neural Networks by Soft Weight Sharing
        Geoffrey E. Hinton and Steven Nowlan

Simplifying Neural Networks by Soft Weight Sharing
        Geoffrey E. Hinton and Steven Nowlan

Error-Correcting Output Codes: A General Method for Improving Multiclass
Inductive Learning Programs
        Thomas G. Dietterich and Ghulum Bakiri

Image Segmentation and Recognition
        John S. Denker and Christopher C. J. Burges

===========================================================

This book grew out of a workshop held under the auspices of the Center
for Non-linear Studies at Los Alamos and the Santa Fe Institute. The
idea for the workshop arose from a perception that there were many
different fields that address supervised learning, but by and large
these fields were not communicating with one another. (Examples of
such fields are neural nets, conventional Bayesian statistics,
conventional sampling theory statistics, computational learning
theory, AI, and machine learning.) In particular, there were many
different mathematical frameworks for addressing supervised
learning. All had their own jargon, their own concerns, and their own
results. And for the most part they weren't interacting.

This was clearly a less than optimal state of affairs; we all have
much to learn from one another, not only in terms of raw mathematical
results, but also (perhaps more importantly) in perceptions of what
the crucial issues are and how they should be addressed.
Unfortunately, although it seems that this problem is abating, the
rate of improvement is quite small. It seems possible that a general
lack of communication amongst its practitioners will characterize
supervised learning theory for some time to come.

The purpose of the workshop was try to (begin to) rectify this
situation. A small group of researchers from several of the different
supervised learning fields was brought together and, in effect, forced
to mingle. The format of the workshop was an intensive two-day session
of talks and discussion. 

This volume is an attempt to try to replicate the success of the
workshop in a broader context. Its purpose is to do for the reader
what the workshop did for its participants: help a practitioner in one
of the fields that make up supervised learning become acquainted with
the relevant work by his or her colleagues in other fields.

Obviously (and unfortunately) it isn't possible to duplicate in a
reader of a book the experience of "an intensive two-day session
.. (of being) forced to mingle ... (with) researchers from different
fields". Given the different format, slightly different means are
needed to achieve the same ends.  Accordingly, it was decided that the
papers in this volume should not so much be a formal compendium of the
talks presented at the workshop as an overview of the work being
performed by the researchers who attended the workshop. Some of the
work represented in these papers hadn't even been completed at the
time of the workshop. Some of the other papers are reprints of work
published shortly before or soon after the workshop. However all of
the papers were chosen by their authors with the same goal in mind: to
help those from other supervised learning fields get acquainted with
the lay of those authors' lands.  Moreover, the instructions to the
authors were that they should not try to provide tutorials on their
individual fields. (There are many other sources for such tutorials.)
Rather they should present current cutting-edge perspectives and work
that provide an intuitive understanding of what their field "is all
about".

===========================================================


The order numbers are 40985 for the hardcover and 40983 for the
paperback. The prices are:

Paperback  0-201-40983-6  $31.25
Hardcover  0-201-40985-2  $59.25


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