book announcements--Obermayer and Jordan

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Hello all,
I wanted to mention two recent publications from MIT Press. For more
information, please visit the URLs listed below. Thank you!

Best,
Jud

Self-Organizing Map Formation
Foundations of Neural Computation
edited by Klaus Obermayer and Terrence J. Sejnowski
http://mitpress.mit.edu/0262650606

This book provides an overview of self-organizing map formation, including
recent developments. Self-organizing maps form a branch of unsupervised
learning, which is the study of what can be determined about the
statistical properties of input data without explicit feedback from a
teacher. The articles are drawn from the journal Neural Computation.

The book consists of five sections. The first section looks at attempts to
model the organization of cortical maps and at the theory and applications
of the related artificial neural network algorithms. The second section
analyzes topographic maps and their formation via objective functions. The
third section discusses cortical maps of stimulus features. The fourth
section discusses self-organizing maps for unsupervised data analysis. The
fifth section discusses extensions of self-organizing maps, including two
surprising applications of mapping algorithms to standard computer science
problems: combinatorial optimization and sorting.

Contributors
J. J. Atick, H. G. Barrow, H. U. Bauer, C. M. Bishop, H. J. Bray, J.
Bruske, J. M. L. Budd, M. Budinich, V. Cherkassky, J. Cowan, R. Durbin, E.
Erwin, G. J. Goodhill, T. Graepel, D. Grier, S. Kaski, T. Kohonen, H.
Lappalainen, Z. Li, J. Lin, R. Linsker, S. P. Luttrell, D. J. C. MacKay, K.
D. Miller, G. Mitchison, F. Mulier, K. Obermayer, C. Piepenbrock, H.
Ritter, K. Schulten, T. J. Sejnowski, S. Smirnakis, G. Sommer, M. Svensen,
R. Szeliski, A. Utsugi, C. K. I. Williams, L. Wiskott, L. Xu, A. Yuille, J.
Zhang.

6 x 9, 394 pp.
paper ISBN 0-262-65060-6
Computational Neuroscience series
A Bradford Book

Graphical Models
Foundations of Neural Computation
edited by Michael I. Jordan and Terrence J. Sejnowski
http://mitpress.mit.edu/0262600420

Graphical models use graphs to represent and manipulate joint probability
distributions. They have their roots in artificial intelligence,
statistics, and neural networks. The clean mathematical formalism of the
graphical models framework makes it possible to understand a wide variety
of network-based approaches to computation, and in particular to understand
many neural network algorithms and architectures as instances of a broader
probabilistic methodology. It also makes it possible to identify novel
features of neural network algorithms and architectures and to extend them
to more general graphical models.

This book exemplifies the interplay between the general formal framework of
graphical models and the exploration of new algorithms and architectures.
The selections range from foundational papers of historical importance to
results at the cutting edge of research.

Contributors
H. Attias, C. M. Bishop, B. J. Frey, Z. Ghahramani, D. Heckerman, G. E.
Hinton, R. Hofmann, R. A. Jacobs, Michael I. Jordan, H. J. Kappen, A.
Krogh, R. Neal, S. K. Riis, F. B. Rodríguez, L. K. Saul, Terrence J.
Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss.

6 x 9, 400 pp.
paper ISBN 0-262-60042-0
Computational Neuroscience series
A Bradford Book

Jud Wolfskill
Associate Publicist
MIT Press
5 Cambridge Center, 4th Floor
Cambridge, MA 02142
617.253.2079
617.253.1709 fax
wolfskil@mit.edu

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