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Machine Learning List Vol. 2 No. 16
Machine Learning List: Vol. 2 No. 16
Thursday, Sept 6, 1990
Contents:
Parallel implementations of clustering algorithms
Book Offers from Morgan Kaufmann and Lawrence Erlbaum Associates
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From: Jeff Shrager <shrager@parc.xerox.COM>
Subject: Re: Parallel clustering (MLL2(15))
>From: Peter Wohl <thsspxw@iitmax.iit.edu>
>Subject: concept clustering implem.
>I am working on implementing (incremental) symbolic conceptual clustering
>on MIMD multiprocessors, so it would run "really fast".
>I would be interested to get references/comments ...
Francine Chen and I have an extended version of ID3 (with chi-square
pruning) with powerset clustering that runs on a CM2. We used it to
generate rules for pronunciation, e.g., that the "t" in "crafts fair" is
sometimes elided. We've reported the algorithms and results in the Feb.
1989 DARPA Speech and NL Workshop (Philadelphia, PA; published by Morgan
Kaufmann in San Mateo, CA); pp. 284-289. We are also using it to induce
Markov speech models (reported in a more recent publication elsewhere that
I don't have the ref for on hand). You can get copied of these papers by
writing to:
Publications Librarian
System Sciences Laboratory
Xerox PARC
3333 Coyote Hill Rd.
Palo Alto, CA, 94304
And ask for: F. Chen & J. Shrager, 1989, and other papers by F. Chen.
Furthermore, we have delivered the implementation to Thinking Machines, but
we haven't any idea what they are going to do with it.
Our ID3 does the tree construction in parallel and clusters at each level
over all possible subsets, so it is very good. However, although it's
certainly faster than a serial implementation (for the powerset clustering,
of course) it's still pretty slow, taking several hours to construct a tree
for examples of single phonemes from 60% of 30,000 entries in the TIMIT
speech database. I don't know if MIMD would be any better than our CM
implementation; it's a pretty inherently SIMD algorithm.
------------------------------
[Note: The next two messages are from book publishers I spoke to at
AAAI. They are extending a 10% discount to readers of ML-LIST on
books related to machine learning. If you know of other publishers
of relevant material who would be willing to offer a discount to
readers of ML-LIST, please have them contact me- Mike]
------------------------------
From: Morgan Kaufmann <morgan@unix.sri.COM>
Subject: Special Offer from Morgan Kaufmann to readers of ML-LIST
Below is a list of recently published Morgan Kaufmann ML books.
The prices shown reflect a special 10% discount to ML list
subscribers and are applicable until the end of September.
MORGAN KAUFMANN PUBLISHERS
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MACHINE LEARNING: AN ARTIFICIAL INTELLIGENCE APPROACH, VOLUME III,
edited by Yves Kodratoff (French National Scientific Research
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1990; 825 pages; Cloth; ISBN 1-55860-119-8 $44.96.
General Issues * Empirical Learning Methods * Analytical Learning
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Revision * Evaluation of KEKADA as an AI Program * Scientific
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COMPUTER SYSTEMS THAT LEARN: CLASSIFICATION AND PREDICTION METHODS
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MATHEMATICAL FOUNDATIONS OF LEARNING MACHINES, by Nils Nilsson
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Sejnowski (Salk Institute) and Hal White (University of California,
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Introduction by Terrence J. Sejnowski and Hal White * Trainable
Pattern Classifiers * Some Important Discriminant Functions: Their
Properties and Their Implementations * Parametric Training Methods
* Some Nonparametric Training Methods for Learning Machines *
Training Theorems * Layered Machines * Piecewise Linear Machines
* Appendix
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END of ML-LIST 2.16
