Copy Link
Add to Bookmark
Report
Machine Learning List Vol. 6 No. 06
Machine Learning List: Vol. 6 No. 6
Sunday, March 6, 1994
Contents:
AAAI-94 Case-Based Reasoning Workshop: CfP
March 18 is the new KDD-94 Workshop paper submission deadline
Workshop announcement: Constructive Induction and Change of Representation
ISIKNH'94 (Knowledge + Neural Heuristics)
The Machine Learning List is moderated. Contributions should be relevant to
the scientific study of machine learning. Mail contributions to ml@ics.uci.edu.
Mail requests to be added or deleted to ml-request@ics.uci.edu. Back issues
may be FTP'd from ics.uci.edu in pub/ml-list/V<X>/<N> or N.Z where X and N are
the volume and number of the issue; ID: anonymous PASSWORD: <your mail address>
----------------------------------------------------------------------
From: aha@aic.nrl.navy.mil
Subject: AAAI-94 Case-Based Reasoning Workshop: CfP
To: ml@ics.uci.edu
Call for Papers: AAAI-94 Workshop on Case-Based Reasoning
Workshop Focus: Evaluation
OBJECTIVES: Several workshops have encouraged theoretical proposals on
case-based reasoning (CBR). This workshop focuses instead on
systematic evaluation of CBR theories, models, systems, and system
components (e.g., retrieval, elaboration, adaptation, and learning).
Our goal is to increase awareness of how to conduct such evaluations
so that they yield useful insights for the design of subsequent systems.
TOPICS: (including but not limited to)
* Comparisons among different CBR systems or components, or between CBR
and other approaches
* Descriptions of CBR applications with emphasis on lessons learned from
user feedback
* Evaluations of CBR components in systems that employ large case bases
and abundant domain-specific knowledge
* Mathematical analyses
* Computational benefits of cognitively plausible CBR models
* Evaluation of the cognitive plausibility of CBR models of human cognition
Researchers and practitioners conducting related work from fields not
always associated with CBR are encouraged to participate. We hope to
accumulate and disseminate knowledge gained from studying CBR-like
algorithms in several fields (e.g., memory-based reasoning, k-nearest
neighbor, etc), thus identifying problem issues and reducing
replications of previous work.
FORMAT: This two-day workshop will include invited talks, paper
presentations, two interactive panel discussions, and a poster session.
The invited talks will include summaries of the performance of
currently fielded CBR systems as well as CBR-related contributions in
areas not traditionally associated with CBR. The first panel will
focus on the utility and limitations of evaluations. The first day
will end with the poster session. The second day will include a panel
on some controversial issue to be determined from the submissions.
Critiques of the field, its direction, and of the workshop's
presentations will also be included on the second day, which will end
with a summary discussion.
ATTENDANCE: Limited to 50 invitees. Additional invitations will be made
as space permits to those who submit written requests.
SUBMISSION REQUIREMENTS: Five copies of each paper should be submitted
in hard copy form. The cover page should contain a title, authors'
names, mailing address, email addresses, telephone numbers, and a
brief abstract. Papers should not exceed twelve single-spaced pages
including figures and bibliography. If using hardcopy, then
double-sided submissions are strongly encouraged. Electronic
submissions (e.g., compressed, uuencoded postscript files) are
also strongly encouraged.
Working notes containing the accepted papers will be distributed.
Authors of accepted papers have the option of including their papers in
the technical report published by AAAI Press for wider distribution.
SUBMISSION DEADLINE: March 18, 1994
NOTIFICATION DATE: April 15, 1994 (note change from 4/8)
DEADLINE FOR CAMERA-READY COPIES: April 29, 1994
Please send submissions to:
David W. Aha
AAAI-94 CBR Workshop
Navy Center for Applied Research in AI (NCARAI)
Naval Research Laboratory
Code 5514
4555 Overlook Ave, SW
Washington, D.C. 20375 USA
aha@aic.nrl.navy.mil
(202) 767-9006 | 767-3172 (FAX)
Workshop Committee:
* David W. Aha
* Christopher G. Atkeson, Massachusetts Institute for Technology,
cga@ai.mit.edu
* Ray Bareiss, Institute for the Learning Sciences, Northwestern
University, bareiss@ils.nwu.edu
* L. Karl Branting, University of Wyoming, karl@eolus.uwyo.edu
* Ashwin Ram, Georgia Institute of Technology, ashwin@pravda.cc.gatech.edu
* Evangelos Simoudis, Lockheed AI Center simoudis@aic.lockheed.com
* Manuela Veloso, Carnegie Mellon University, mmv@cs.cmu.edu
Please contact me if you have any questions/suggestions.
------------------------------
Date: Tue, 01 Mar 1994 15:52:32 -0500 (EST)
From: "R. Uthurusamy" <SAMY@gmr.com>
Subject: March 18 is the new KDD-94 Workshop paper submission deadline
Note: March 18, 1994 is the new paper submission date for KDD-94 Workshop
--------------
since AAAI has published this later deadline and we intend to honor it.
You are encouraged to submit earlier if at all possible. Thanks.
-- sam
============================================================================
C a l l F o r P a p e r s
============================================================================
KDD-94: AAAI Workshop on Knowledge Discovery in Databases
Seattle, Washington, July 31-August 1, 1994
===========================================
Knowledge Discovery in Databases (KDD) is an area of common interest for
researchers in machine learning, machine discovery, statistics, intelligent
databases, knowledge acquisition, data visualization and expert systems. The
rapid growth of data and information created a need and an opportunity for
extracting knowledge from databases, and both researchers and application
developers have been responding to that need. KDD applications have been
developed for astronomy, biology, finance, insurance, marketing, medicine,
and many other fields. Core Problems in KDD include representation issues,
search complexity, the use of prior knowledge, and statistical inference.
This workshop will continue in the tradition of the 1989, 1991, and 1993 KDD
workshops by bringing together researchers and application developers from
different areas, and focusing on unifying themes such as the use of domain
knowledge, managing uncertainty, interactive (human-oriented) presentation,
and applications. The topics of interest include:
Applications of KDD Techniques
Interactive Data Exploration and Discovery
Foundational Issues and Core Problems in KDD
Machine Learning/Discovery in Large Databases
Data and Knowledge Visualization
Data and Dimensionality Reduction in Large Databases
Use of Domain Knowledge and Re-use of Discovered Knowledge
Functional Dependency and Dependency Networks
Discovery of Statistical and Probabilistic models
Integrated Discovery Systems and Theories
Managing Uncertainty in Data and Knowledge
Machine Discovery and Security and Privacy Issues
We also invite working demonstrations of discovery systems. The workshop
program will include invited talks, a demo and poster session, and panel
discussions. To encourage active discussion, workshop participation will be
limited. The workshop proceedings will be published by AAAI. As in previous
KDD Workshops, a selected set of papers from this workshop will be considered
for publication in journal special issues and as chapters in a book.
Please submit 5 *hardcopies* of a short paper (a maximum of 12 single-spaced
pages, 1 inch margins, and 12pt font, cover page must show author(s) full
address and E-MAIL and include 200 word abstract + 5 keywords) to reach the
workshop chairman on or before March 1, 1994.
Usama M. Fayyad (KDD-94) | Fayyad@aig.jpl.nasa.gov
AI Group M/S 525-3660 |
Jet Propulsion Lab | (818) 306-6197 office
California Institute of Technology | (818) 306-6912 FAX
4800 Oak Grove Drive |
Pasadena, CA 91109 |
************************************* I m p o r t a n t D a t e s **********
* Submissions Due: March 18, 1994 *
* Acceptance Notice: April 8, 1994 Final Version due: April 29, 1994 *
******************************************************************************
Program Committee
=================
Workshop Co-Chairs:
Usama M. Fayyad (Jet Propulsion Lab, California Institute of Technology)
Ramasamy Uthurusamy (General Motors Research Laboratories)
Program Committee:
Rakesh Agrawal (IBM Almaden Research Center)
Ron Brachman (AT&T Bell Laboratories)
Leo Breiman (University of California, Berkeley)
Nick Cercone (University of Regina, Canada)
Peter Cheeseman (NASA AMES Research Center)
Greg Cooper (University of Pittsburgh)
Brian Gaines (University of Calgary, Canada)
Larry Kerschberg (George Mason University)
Willi Kloesgen (GMD, Germany)
Chris Matheus (GTE Laboratories)
Ryszard Michalski (George Mason University)
Gregory Piatetsky-Shapiro (GTE Laboratories)
Daryl Pregibon (AT&T Bell Laboratories)
Evangelos Simoudis (Lockheed Research Center)
Padhraic Smyth (Jet Propulsion Laboratory)
Jan Zytkow (Wichita State University)
============================================================================
------------------------------
Date: Fri, 4 Mar 94 15:26:20 EST
From: Tom Fawcett <fawcett@nynexst.com>
Subject: Workshop announcement: Constructive Induction and Change of Representation
ML '94 WORKSHOP
Constructive Induction and Change of Representation
An appropriate representation is critical to the success of an inductive
learning task. In difficult learning problems (eg protein folding, word
pronunciation, gene identification), considerable human effort is often
required to identify useful terms of the representation language. In an
effort to make learning more autonomous, researchers have investigated the
problem of generating or modifying new representations automatically.
The past five years have seen a significant increase in the amount of work in
this area. Some methods developed have been able to effect increases in
classification accuracy. Others are able to derive features similar to those
discovered previously by humans. Still other systems have demonstrated
impressive performance improvement through the construction of new
representations.
In spite of these successes, we are still far from understanding the range and
limitations of current methods, or the kind of representation change that
real-world domains may require. The objective of this workshop is to examine
issues in current work and to review progress made so far. The workshop will
also serve as a forum for the exchange of ideas among researchers actively
working on these issues. Topics of interest include, but are not limited to,
the following:
- Empirical approaches and the use of inductive biases
- Use of domain knowledge in the construction and evaluation of new terms
- Construction of or from relational predicates
- Introduction of new terms by analytic theory revision systems
- Unsupervised learning and credit assignment in constructive induction
- Interpreting hidden units as constructed features
- New terms as indices in instance-based learning or case-based reasoning
- Constructive induction in human learning
- Experimental studies of constructive induction systems
- Theoretical proofs, frameworks, and comparative analyses
- Comparison of techniques from empirical learning, analytical
learning, classifier systems, and neural networks
WORKSHOP FORMAT
The workshop will be held on Sunday, July 10th. Attendance will be
open. The workshop will consist of presentations of accepted papers
and a final panel discussion. The panel will recap the workshop and
discuss the state of constructive induction and current open
questions.
SUBMISSIONS
Paper submissions should not exceed 3000 words (about six single-spaced pages,
including figures and tables, but excluding bibliography). Four copies of
each paper should be sent to the contact address below. Alternatively, one
copy of a postscript file may be sent via e-mail. Each paper should include
an e-mail contact address of one of the authors. The papers will comprise a
set of working notes, copies of which will be available at the workshop.
We encourage descriptions of work in progress as well as position papers.
Authors are encouraged to evaluate their systems on real-world domains and
to critique their methods with respect to the following questions:
- In your system, what is the relationship between the feature generation and
induction? Can the feature generation method be adapted to other forms of
induction?
- How does the method evaluate or select the features that it generates?
- Is the method sensitive to the cost of the features? Can it create
features of unbounded expense?
- What real-world domain(s) has the method been applied to? What
characteristics of each domain makes feature generation useful or necessary?
For what general class of domain might the method be useful?
- Can the method exploit existing domain knowledge? What forms of domain
knowledge can be exploited?
- What features are already known for the domain being addressed? Can the
method re-derive any of them?
- What forms (eg, propositional, relational, numerically weighted) can the
generated features take? Does this limit the method?
SCHEDULE
Paper submissions due 25 April
Decisions made, submitters get feedback 22 May
Final working-note submissions due 15 June
Workshop 10 July
PROGRAM COMMITTEE
Tom Fawcett (chair), NYNEX Science and Technology
James Callan, University of Massachusetts at Amherst
Chris Matheus, GTE Laboratories Inc.
Ryszard Michalski, George Mason University
Michael Pazzani, University of California at Irvine
Larry Rendell, University of Illinois at Urbana-Champaign
Rich Sutton, GTE Laboratories Inc.
CONTACT ADDRESS
Tom Fawcett
NYNEX Science and Technology
500 Westchester Ave.
White Plains, NY 10604
e-mail: fawcett@nynexst.com
------------------------------
Date: Tue, 1 Mar 94 15:56:42 -0500
From: fu@cis.ufl.edu
Subject: ISIKNH'94 (Knowledge + Neural Heuristics)
ISIKNH'94 (Advance Program and Registration Information)
(International Symposium on Integrating Knowledge and Neural Heuristics)
Sponsored by AAAI and University of Florida
Time: May 9-10 1994; Place: Pensacola Beach, Florida, USA.
========================================================================
Keynote Speech:
May 9, 9:00-9:45 a.m.
"Representation, Cognitive Architectures and Knowledge and Symbol Levels"
B. Chandrasekaran
========================================================================
========================================================================
Plenary Speech:
May 9, 10:00-10:45 a.m.
"Fuzzy Logic as a Basis for Knowledge Representation in Neural Networks"
Ronald R. Yager
========================================================================
========================================================================
Plenary Speech:
May 9, 11:00-11:45 a.m.
"Hybrid Models for Fuzzy Control"
Jim Bezdek
========================================================================
**** Lunch Break ****
------------------------------------------------------------------------
Technical Session 1: (Integration Methodology I)
Chair: Armando F. da Rocha
May 9, 1:15-2:00 p.m.
``Integrating temporal symbolic knowledge and recurrent networks''
Christian W. Omlin, C. Lee Giles
``Implementing schemes and logics in connectionist models''
Ron Sun
``Integrating rules and neurocomputing for knowledge representation''
Ioannis Hatzilygeroudis
------------------------------------------------------------------------
------------------------------------------------------------------------
Technical Session 2: (Learning)
Chair: Ron Sun
May 9, 2:15-3:00 p.m.
``Symbolic knowledge from unsupervised learning''
Tharam S. Dillon, S. Sestito, M. Witten, M. Suing
``Genetically refining topologies of
knowledge-based neural networks''
David W. Opitz, Jude W. Shavlik
``On using decision tree as feature selector for feed-forward
neural networks''
Selwyn Piramuthu, Michael Shaw
------------------------------------------------------------------------
**** Snack Break ****
------------------------------------------------------------------------
Technical Session 3: (Fuzziness and Uncertainty)
Chair: Lee Giles
May 9, 3:30-4:15 p.m.
``Modifying network architectures for certainty-factor
rule-base revision''
Jeffrey Mahoney, Raymond Mooney
``Learning EMYCIN semantics''
K.D. Nguyen, R.C. Lacher
``Special fuzzy relational methods for the recognition of speech
with neural networks''
Carlos A. Reyes, Wyllis Bandler
------------------------------------------------------------------------
------------------------------------------------------------------------
Technical Session 4: (Integration Methodology II)
Chair: Ron Sun
May 9, 4:30-5:30 p.m.
``Learning knowledge and strategy of a generic neuro-expert system model''
Rajiv Khosla, T. Dillon
``Integrating symbolic and neural methods for building intelligent systems''
Ricardo Jose Machado, Armando Freitas da Rocha
``Modular integration of connectionist and symbolic processing
in knowledge-based systems''
Melanie Hilario
``Symbolic computation with monotonic maps of the interval''
Ron Bartlett, Max Garzon
------------------------------------------------------------------------
------------------------------------------------------------------------
Poster Session:
May 9, 1:30-4:30 p.m.
``The KoDiag system Case-based diagnosis with Kohonen networks''
Jurgen Rahmel, A. von Wangenheim
``Deriving conjunctive classification rules from neural networks''
Chris Nikolopoulos
``Generalization and fault tolerance in rule-based neural networks''
Hyeoncheol Kim, L. Fu
``Low level feature extraction and hidden layer neural network training''
T. Windeatt, R.G. Tebbs
``Sleeping staging by expert networks''
Hui-Huang Hsu, L. Fu, J. Principe
``Comparison of neural network and symbolic approaches
for predicting electricity generation requirements''
Terry Janssen, Eric Bleodorn, Ron Capone, Sue Kimbrough
``Reconciling connectionism with symbolism''
Roman Pozarlik
------------------------------------------------------------------------
MAY 10, 1994:
Registration: 7:30-11:00a.m.
========================================================================
Plenary Speech:
May 10, 9:00-9:45 a.m.
"Teaching the Multiplication Tables to a Neural Network: Flexibility vs. Accuracy"
James Anderson
========================================================================
========================================================================
Plenary Speech:
May 10, 10:00-10:45 a.m.
"Words and Weights: What the Network's Parameters
Tell the Network's Programmers"
Steve Gallant
========================================================================
########################################################################
Panel Discussions:
May 10, 11:00 a.m. - 12:20 p.m.
"The Future Direction of AI"
Chair: Chris Lacher
Panelists: James Anderson, Steve Gallant, Ronald Yager, Ron Sun,
Lawrence Bookman.
########################################################################
**** Lunch Break ****
------------------------------------------------------------------------
Technical Session 5: (Application Methodology I--Finance and Medicine)
Chair: Sylvian R. Ray
May 10, 1:30-2:15 p.m.
``Building a knowledge base from on-line corpora''
Lawrence A. Bookman
``Multivariate prediction using prior knowledge and
neural heuristics''
Kazuhiro Kohara, Tsutomu Ishikawa
``Applying artificial neural networks to medical knowledge domain''
Harry Burke, Philip Goodman, David Rosen
------------------------------------------------------------------------
------------------------------------------------------------------------
Technical Session 6: (Application Methodology II--Engineering)
Chair: Lawrence Bookman
May 10, 2:30-3:15 p.m.
``Integrating knowledge from multichannel signals''
Sylvian R. Ray
``Using partitioned neural nets and heuristics for
optical character recognition''
Kai Bolik, Steven Shoemaker, Divyendu Sinha, Miriam Tausner
``An expert network approach for material selection''
Vivek Goel, Jianhua Chen
------------------------------------------------------------------------
**** Snack Break ****
------------------------------------------------------------------------
Technical Session 7: (Language, Psychology, and Cognitive Science)
Chair: Steven Walczak
May 10, 3:45-4:30 p.m.
``From biological learning to machine learning''
Iver H. Iversen
``RAAMs that can learn to encode words from
a continuous stream of letters''
Kenneth A. Hester, Michael Bringmannm,
David Langan, Marino Niccolai, William Nowack
``The psychology of associative and symbolic reasoning''
Steven Sloman
------------------------------------------------------------------------
------------------------------------------------------------------------
Technical Session 8: (Integration Methodology III)
Chair: Ioannis Hatzilygeroudis
May 10, 4:45-5:30 p.m.
``Situation awareness assessments as a means of defining
learning tasks for neural networks''
Thomas English
``Integrating neural networks and expert systems for
intelligent resource allocation in academic admissions''
Steven Walczak
``Rule constraint and game playing heuristic embedded
into a feed forward neural network''
Walter H. Johnson
------------------------------------------------------------------------
*******
Wrap-Up
*******
---------------------------------------------------------------------
Please send your registration including a registration fee to:
Rob Francis
ISIKNH'94
DOCE/Conferences
2209 NW 13th Street, STE E
University of Florida
Gainesville, FL 32609-3476
USA
(Phone: 904-392-1701; fax: 904-392-6950)
[Registration fee: $250 by April 8, $300 on site, $150 for students]
---------------------------------------------------------------------
---------------------------------------------------------------------
For registration, please submit the following
information to the above address:
NAME: _______________________________________
ADDRESS: ____________________________________
_____________________________________________
_____________________________________________
_____________________________________________
_____________________________________________
INSTITUTION/COMPANY: ________________________
PHONE: ______________________________________
FAX: ________________________________________
E-MAIL: _____________________________________
---------------------------------------------------------------------
------------------------------
End of ML-LIST (Digest format)
****************************************
