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Machine Learning List Vol. 2 No. 22
Machine Learning List: Vol. 2 No. 22
Tuesday, Nov 13, 1990
Contents:
CBR Workshop
book offer
NIPS '90 workshop comparing decision trees and neural nets
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------------------------------
Date: Tue, 6 Nov 90 08:17:40 CST
From: Ray Bareiss <bareiss@zettel.ils.nwu.edu>
Subject: CBR Workshop
CALL FOR PAPERS
1991 DARPA Workshop on Case-Based Reasoning
May 8-10, 1991
The Information Science and Technology Office of the Defense Advanced Research
Projects Agency is sponsoring a Workshop on Case-Based Reasoning for invited
researchers and interested government employees on May 8-10, 1991 at the
Radisson Plaza Hotel in Alexandria, Virginia.
The purpose of the workshop is to assess the state of the art and provide a
snapshot of ongoing research in Case-Based Reasoning. The workshop is intended
to bring active researchers together to review the latest research results in
this field, to keep the government research community abreast of current
technology, and to discuss the future of the case-based paradigm for a new
generation of knowledge-based systems.
The 1989 workshop was centered around a set of issue-oriented panels that
discussed fundamental problems of Case-Based Reasoning, including case
representation, indexing, similarity assessment, and case adaptation. The goal
of this year's workshop is to discuss the progress of researchers towards
solving these problems in the context of Case-Based Reasoning systems.
Consequently, the program committee will prefer papers that discuss implemented
systems over those that discuss unimplemented ideas. Papers that are concrete
and concise will be preferred over those that are philosophical and/or
abstract. Since AI research is largely an experimental science, it is
anticipated that these papers will describe experiments performed and the
measures applied to evaluate the experimental results. Papers that discuss
specific applications where integration and/or scalability issues were solved
in novel ways and program demonstrations are also encouraged. Researchers
interested in demonstrating programs should contact Ray Bareiss to arrange
for the needed computer equipment.
A second goal of the workshop will be to provide challenges for future research
by encouraging interaction between researchers and potential beneficiaries of
their research, such as industrial designers, instructional designers, and
builders of large software systems.
Researchers who would like to present at the workshop are asked to submit five
copies of their papers to:
CBR91 Workshop
c/o Ray Bareiss
Institute for the Learning Sciences
Northwestern University
1890 Maple Avenue
Evanston, IL 60201
Submitted papers should be camera-ready, not exceeding twelve single-spaced
pages including figures and bibliography. Formatting instructions will be sent
via surface mail along with a hard copy of this announcement. It is the
intention of the program committee to accept papers for publication as
submitted, i.e., without revision. People who wish to attend without
presenting should submit a brief statement of interest.
The submission deadline is January 18, 1991; notification of acceptance will
be made by February 28, 1991.
As with other DARPA/ISTO sponsored workshops, a full proceedings of the
workshop will be made available to those who attend.
Workshop Program Committee:
Ray Bareiss, Northwestern University (chair)
Kris Hammond, University of Chicago
Janet Kolodner, Georgia Institute of Technology
Bill Mark, Lockheed AI Center
Chris Riesbeck, Northwestern University
Edwina Rissland, University of Massachusetts
Katia Sycara, Carnegie Mellon University
Note: We encourage distribution of this announcement to interested colleagues
who are active in case-based reasoning research. Contact Romina Fincher by
telephone (703)614-4001 or email fincher@darpa.mil if you would like copies
of this announcement sent elsewhere.
------------------------------
From: abg@vax135.att.COM (Allen Ginsberg)
Date: Mon, 5 Nov 90 10:05:20 est
Subject: book offer
This is a "followup" to Alberto Segre's posting about books to
review.
My book, "Automatic Refinement of Expert System Knowledge Bases,"
appeared in July 1988 in the Research Notes in AI series. To
my knowledge it has never been reviewed in print anywhere. (If
anybody has seen a review of it, I would appreciate a reference.)
In order to correct this oversight, I hereby offer to send the first 20 people
who respond a free copy of the book (signed by the author hisself) provided
the suckers, I mean, persons, in question promise to write a review of it and
submit the review for publication in some AI, or other appropriate, journal,
magazine, rag, etc.
Actually this is not just a matter of soothing a slightly bruised ego: sales
are slumping and baby needs a new pair of shoes. Come on guys, you gotta help
me out!
Allen Ginsberg
abg@vax135.att.com
phone: 201-949-5921
------------------------------
From: "Lorien Y. Pratt" <pratt@paul.rutgers.edu>
Subject: Announcing a NIPS '90 workshop comparing decision trees and neural nets
Date: 13 Nov 90 14:15:12 GMT
Neural Networks and Decision Tree Induction:
Exploring the relationship between two research areas
A NIPS '90 workshop, 11/30/1990 or 12/1/1990, Keystone, Colorado
Workshop Co-Chairs:
L. Y. Pratt and S. W. Norton
The fields of Neural Networks and Machine Learning have evolved
separately in many ways. However, close examination of multilayer
perceptron learning algorithms (such as Back-Propagation) and decision
tree induction methods (such as ID3 and CART) reveals that there is
considerable convergence between these subfields. They address similar
problem classes (inductive classifier learning) and can be
characterized by a common representational formalism of hyperplane
decision regions. Furthermore, topical subjects within both fields are
related, from minimal trees and network reduction schemes to
incremental learning.
In this workshop, invited speakers from the Neural Network and
Machine Learning communities will discuss their empirical and
theoretical comparisons of the two areas, and then present work at
the interface between these two fields which takes advantage of the
potential for technology transfer between them. In a discussion
period, we'll discuss our conclusions, comparing the methods along
the dimensions of representation, learning, and performance. We'll
debate the ``strong convergence hypothesis'' that these two
research areas are really studying the same problem.
Schedule of talks:
AM:
7:30-7:50 Lori Pratt Introductory remarks
7:50-8:10 Tom Dietterich Evidence For and Against Convergence:
Experiments Comparing ID3 and BP
8:15-8:35 Les Atlas Is backpropagation really better than
classification and regression trees?
8:40-9:00 Ah Chung Tsoi Comparison of the performance of some popular
machine learning algorithms: CART, C4.5, and
multi-layer perceptrons
9:05-9:25 Ananth Sankar Neural Trees: A Hybrid Approach to Pattern
Recognition
PM:
4:30-4:55 Stephen Omohundro A Bayesian View of Learning with Tree
Structures and Neural Networks
5:00-5:20 Paul Utgoff Linear Machine Decision Trees
5:25-5:45 Terry Sanger Basis Function Trees as a Generalization of
CART, MARS, and Other Local Variable Selection
Techniques
5:50-6:30 Discussion, wrap-up
------------------------------------------------------------------------------
L. Y. Pratt S. W. Norton
pratt@paul.rutgers.edu, norton@learning.siemens.com
Rutgers University Computer Science Dept. Siemens Corporate Research
New Brunswick, NJ 08903. 755 College Road East
(201) 932-4634 Princeton, NJ 08540
-------------------------------------------------------------------
L. Y. Pratt Computer Science Department
pratt@paul.rutgers.edu Rutgers University
Hill Center
(201) 932-4634 (Hill Center office) New Brunswick, NJ 08903, USA
(201) 846-4766 (home)
------------------------------
END of ML-LIST 2.22
