Path: utzoo!utgpu!news-server.csri.toronto.edu!bonnie.concordia.ca!thunder.mcrcim.mcgill.edu!snorkelwacker.mit.edu!apple!usc!samsung!munnari.oz.au!brolga!uqcspe!batserver.cs.uq.oz.au!bakker From: bakker@batserver.cs.uq.oz.au (Paultje Bakker) Newsgroups: comp.ai.neural-nets Subject: Re: Introductory books... Keywords: books Message-ID: <6929@uqcspe.cs.uq.oz.au> Date: 23 Jan 91 21:46:50 GMT References: <1991Jan22.173911.779@scrumpy@.bnr.ca> Sender: news@uqcspe.cs.uq.oz.au Reply-To: bakker@batserver.cs.uq.oz.au Organization: Computer Science Department, The University of Queensland, Brisbane, Australia Lines: 146 In article <1991Jan22.173911.779@scrumpy@.bnr.ca> bnrmtl!stu4@larry.mcrcim.mcgill.edu writes: >I am wondering if anyone could suggest some introductory >level books in the area of Neural Networks. Preferably >something within a student's budget. > (Yes, Virginia, this request has come up before. I might turn this into a regular 3-monthly posting... please mail any new additions to bakker@batserver.cs.uq.oz.au. Comments on the books would also be very useful.) 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 and Nathan Brown. Paul Bakker bakker@batserver.cs.uq.oz.au -- --Paul Bakker email: bakker@batserver.cs.uq.oz.au --Dept. of Scatology "Love between the ugly --University of Qld Is the most beautiful love of all" --Gondwanaland - T. Rundgren