bakker@batserver.cs.uq.oz.au (Paultje Bakker) (10/01/90)
Thanks to everyone for the great response! So, here follows..... SUMMARY OF RESPONSES to the request for: "A good, general, *readable* introduction to neural networks" October 1990. ******************************************* Rumelhart D.E and McClelland J.L Parallel Distributed Processing Vols 1 & 2 MIT, Cambridge 1986 It's quite readable, and affordable (about $65 for both volumes). A companion volume 'Explorations in PDP' by McClelland is written in a tutorial style, and includes 2 diskettes of NN simulation programs that can be compiled on MS-DOS or Unix (and they do too !) A paper by Rumelhart et.al published in Nature at the same time (vol 323 October 1986) gives a very good potted explanation of backprop NN's. It gives sufficient detail to write your own NN simulation. ----------------------------------- I Aleksander, H Morton: An Introduction to Neural Computing Chapman and Hall, 1990. ----------------------------------- Books: >From CS point of view: %A P. D. Wasserman %T Neural Computing: Theory and Practice %I Van Nostrand Reinhold %C New York %D 1989 >From AI point of view: %A M. Zeidenberg %C Chichester %D 1990 %I Ellis Horwood, Ltd. %T Neural Networks in Artificial Intelligence >From Psych point of view (note the bulk): %A D. E. Rumelhart %A J. L. McClelland %D 1986 %I The MIT Press %K PDP-1 %T Parallel Distributed Processing: Explorations in the Microstructure of Cognition %o (volume 1) %A J. L. McClelland %A D. E. Rumelhart %D 1986 %I The MIT Press %K PDP-2 %T Parallel Distributed Processing: Explorations in the Microstructure of Cognition %o (volume 2) %A J. L. McClelland %A D. E. Rumelhart %D 1988 %I The MIT Press %T Explorations in Parallel Distributed Processing: Computational Models of Cognition and Perception Papers: %A R. P. Lippmann %D April 1987 %J IEEE Transactions on Acoustics, Speech, and Signal Processing %V 2 %N 4 %P 4--22 %T An introduction to computing with neural nets %X Much acclaimed as an overview of neural networks, but rather inaccurate on several points. The categorization into binary and continuous-valued input neural networks is rather arbitrary, and may work confusing for the unexperienced reader. Not all networks discussed are of equal importance. %A G. E. Hinton %T Connectionist learning procedures %J Artificial Intelligence %V 40 %D 1989 %P 185--234 %X One of the better neural networks overview papers, although the distinction between network topology and learning algorithm is not always very clear. Could very well be used as an introduction to neural networks. ------------------------ D. Wunsch (ed.) (1991) Neural Networks : An Introduction. ------------------------ "Naturally Intelligent Systems" by Caudill, Maureen and Charles Butler. Cambridge, Massachusetts: MIT Press, (1990). ISBN 0-262-03156-6 (about 300 pages) ------------------------- Yoh-Han Pao, Adaptive Pattern Recognition and Neural Nets, c. 1989 by Addison-Wesley Publishing Company, Inc. ------------------------ Neural Computing an Introduction by R. Beale and T. Jackson. It's $30.00 and published by Adam Hilger (ISBN 0-85274-262-2). It's clearly written. Lots of hints as to how to get the adaptive models covered to work (not always well explained in the original sources). Consistent mathematical terminology. Covers perceptrons, error-backpropagation, Kohonen self-org model, Hopfield type models, ART, and associative memories. ************************************ Wasserman seemed to be the most popular choice. Thanks to James Tizard, Patrick van der Smagt, Guszti Bartfai, Don Wunsch, Andy, Lilly Spirkovska, Nathan Brown, and others. Paul Bakker bakker@batserver.cs.uq.oz.au -- Paul Bakker | Internet bakker@batserver.cs.uq.oz.au Dept of Computer Science| Bitnet: bakker%batserver.cs.uq.oz.au@uunet.uu.net Uni of Qld | JANET: bakker%batserver.cs.uq.oz.au@uk.ac.ukc Australia | EAN: bakker@batserver.cs.uq.oz
reynolds@bucasd.bu.edu (John Reynolds) (10/03/90)
A clearly written introduction to the field is Patrick K. Simpson's Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations- 1st edition. Pergamon Press (1990) It presents the following paradigms/models in a unified notation which facilitates comparison: UNSUPERVISED FEEDFORWARD LEARNING AND RECALL SYSTEMS: learning matrix, drive reinforcement, sparse distributed memory, linear associative memory, optimal linear associative memory, fuzzy associative memory, learning vector quantizer, and counter propagation UNSUPERVISED FEEDBACK LEARNING AND RECALL SYSTEMS: additive grossberg, shunting grossberg, binary adaptive resonance theory (ART1), analog adaptive resonance theory (ART2), discrete autocorrelator, continuous hopfield, discrete bidirectional associative memory, adaptive bidirectional associative memory, temporal associative memory SUPERVISED FEEDBACK LEARNING AND RECALL SYSTEMS: brain-state-in-a-box, fuzzy cognitive map SUPERVISED FEEDFORWARD LEARNING AND RECALL SYSTEMS: perceptron, adaline/madaline, backpropagation, boltzmann machine, cauchy machine, adaptive heuristic critic, associative reward-penalty, avalanche matched filter It also describes in precis form the past contributions of the following researchers, and indicates their present research interests: McCulloch and Pitts, Hebb, Minsky, Uttley, Rosenblatt, Widrow, Steinbuch, Grossberg, Amari, Anderson, Longuet-Higgins, Willshaw, and Bunemann, Fukushima, Klopf, Kohonen, Copper, Erlbaum, Sejnowski, McClelland, Rumelhart, Sutton and Barto, Feldman, Ballard, Hecht-Nielsen, Hopfield and Tank, Mead, and Kosko.