Series Announcement and Call for Proposals

[Robert Prior]

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The MIT Press  -  Adaptive Computation and Machine Learning Series
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Tom Dietterich, Series Editor

Christopher Bishop, David Heckerman, Michael Jordan, and Michael Kearns,
Associate Editors

The goal of building systems that can adapt to their environments and learn
from their experience has attracted researchers from many fields, including
computer science, engineering, mathematics, physics, neuroscience, and
cognitive science. Out of this research has come a wide variety of learning
techniques, including methods for learning decision trees, decision rules,
neural networks, statistical classifiers, and probabilistic graphical
models. These learning methods have the potential to transform many
industrial and scientific fields.  Many successful and profitable
applications have already been developed.

The researchers in these various areas have also produced several different
theoretical frameworks for understanding these methods, such as
computational learning theory, Bayesian learning theory, classical
statistical theory,
minimum description length theory, and statistical mechanics approaches.
These theories provide insight into experimental results and help to guide
the development of improved learning algorithms.

Recently, the many separate research communities have begun to converge on
a common set of issues surrounding supervised, unsupervised, and
reinforcement learning problems. A goal of the series is to promote the
unification of the many diverse strands of machine learning research and to
foster high quality research and innovative applications.

This book series will publish works of the highest quality that advance the
understanding and practical application of machine learning and adaptive
computation. Books appropriate for the series include:
*   Research monographs on any of the topics listed above
*   Textbooks at the introductory or advanced level
*   How-to books aimed at practitioners
*   Books intended to introduce the main goals and challenges of this
    area to a general technical audience.


For information on the submission of proposals and manuscripts, please
contact the editor, the publisher, or any of the associate editors listed
above:

Thomas G. Dietterich                    Robert V. Prior
Computer Science Department             The MIT Press
Oregon State University                 5 Cambridge Center
Corvallis, OR  97331-3202               Cambridge, MA 02142
(541) 737-5559                          (617) 253-1584
Fax: (541) 737-3014                     Fax: (617) 258-6779
tgd at cs.orst.edu                         prior at mit.edu