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Machine Learning List Vol. 5 No. 21
Machine Learning List: Vol. 5 No. 21
Monday, October 4, 1993
Contents:
ML '94 conference announcement
COLT'94: call for papers
CFP: AAAI-94 Spring Symposium on Goal-Driven Learning
PEBLS 2.0
ML '94 conference announcement (latex format)
The Machine Learning List is moderated. Contributions should be relevant to
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----------------------------------------------------------------------
Date: Wed, 29 Sep 93 13:23 EDT
From: William Cohen <wcohen@research.att.COM>
To: ml@ics.uci.edu
Subject: ML '94 conference announcement
CALL FOR PAPERS - ML94
Eleventh International Conference on Machine Learning
New Brunswick, New Jersey
July 10-13, 1994
The Eleventh International Conference on Machine Learning (ML94) will
be held at the New Brunswick campus of Rutgers University during July
11-13, 1994, with workshops taking place on July 10th. The
conference will be co-located with the Seventh Annual Conference on
Computational Learning Theory (COLT94), which will be held July
12-15. We invite submissions to ML94 from researchers in machine
learning or related fields, such as psychology, statistics, or
neuroscience. The conference will include presentations of refereed
papers and invited talks.
REVIEW CRITERIA. Each submitted paper will be reviewed by at least two
members of the program committee, and be judged on clarity,
significance, and originality. Submissions should contain new results
that have not been published previously. Submissions to ML94 may be
submitted to other conferences, but if so a statement to this effect
must appear on the title page.
PAPER FORMAT. Submissions must be clearly legible, with good quality
print. Papers are limited to a total of twelve (12) pages, excluding
title page and bibligraphy, but including all tables and figures.
Papers must be printed on 8 1/2" x 11" or A4 paper using 12 point type
(10 characters per inch for typewriters). Each page must have a
maximum of 38 lines and an average of 75 characters per line
(corresponding to the LaTeX article style, 12 point). Papers not
adhering to this format may be returned without review. Double-sided
printing is strongly encouraged. The title page of each paper must
include the e-mail and postal addresses of all authors, an abstract,
and one or more keywords from the following list to aid in the
reviewing process: analogy, Bayesian learning, case-based reasoning,
cognitive modeling, computational learning theory, concept formation,
decision trees, discovery, explanation-based learning, genetic
algorithms, inductive logic programming, instance-based learning,
knowledge acquisition, minimum description length, neural networks,
reformulation, reinforcement learning, scientific theory formation,
speedup learning, theory refinement, unsupervised learning,
constructive induction, multi-strategy learning.
REQUIREMENTS FOR SUBMISSION. Authors must submit five copies of their
papers to the address below; electronic or FAX submission is not
acceptable. Papers must be received by February 8, 1994. Notification
of acceptance or rejection will be mailed to the first (or designated)
author by March 25, 1994. Camera-ready copy of accepted papers will
be due April 26, 1994. Send conference paper submissions to: William
W. Cohen, ATT Bell Laboratories, 600 Mountain Avenue, Room 2A-427,
Murray Hill, NJ 07974 (908)-582-2092.
INFORMAL WORKSHOPS. Proposals are invited for informal workshops in
areas of interest related to machine learning. Send a two-page
proposal to: Russell Greiner, ML94 Workshop Chair, Siemens Corporate
Research, 755 College Road East, Princeton, NJ 08540, by December 1,
1993, indicating the organizer(s), nature and objective of the
proposed workshop, and the likely number of attendees.
GENERAL INQUIRIES:
ml94@cs.rutgers.edu
CONFERENCE CO-CHAIRS:
William W. Cohen Haym Hirsh
ATT Bell Laboratories Department of Computer Science
600 Mountain Avenue Rutgers University
Murray Hill, NJ 07974 New Brunswick, NJ 08903
wcohen@research.att.com hirsh@cs.rutgers.edu
PROGRAM COMMITTEE:
David Aha, NRL
Yuichiro Anzai, Keio U.
Eric Baum, NEC
Francesco Bergadano, U. Torino
Wray Buntine, NASA
Jason Catlett, ATT
Marie des Jardins, SRI
Tom Dietterich, Oregon St. U.
Doug Fisher, Vanderbilt U.
John Grefenstette, NRL
Russ Greiner, Siemens
Geoff Hinton, U. Toronto
Leslie Kaelbling, Brown U.
Dennis Kibler, UCI
John Laird, U. Michigan
Sridhar Mahadevan, S. Florida
Hiroshi Motoda, Hitachi
Ray Mooney, U. Texas
Katharina Morik, U. Dortmund
Mike Pazzani, UCI
Lenny Pitt, U. Illinois
Lorien Pratt, Colorado S. of Mines
Armand Prieditis, UC/Davis
Paul Rosenbloom, USC/ISI
Stuart Russell, UCB
Lorenza Saitta, U. Torino
Claude Sammut, U. New S. Wales
Cullen Schaffer, Hunter College
Rich Sutton, GTE
Paul Utgoff, U. Mass
Stefan Wrobel, GMD
Steve Whitehead, GTE
Manuela Veloso, CMU
Kenji Yamanishi, NEC
WORKSHOP CHAIR:
Russell Greiner
Siemens Corporate Research
755 College Road East
Princeton, NJ 08540
greiner@learning.scr.siemens.com
LOCAL ARRANGEMENTS:
Priscilla Rasmussen
Department of Computer Science
Rutgers University
New Brunswick, NJ 08903
rasmusse@cs.rutgers.edu
------------------------------
Date: Sat, 2 Oct 93 01:56:58 -0400
From: Ming Li <mli@math.uwaterloo.ca>
Subject: COLT'94: call for papers
CALL FOR PAPERS---COLT 94
Seventh ACM Conference on
Computational Learning Theory
New Brunswick, New Jersey
July 12--15, 1994
The Seventh ACM Conference on Computational Learning Theory (COLT
94) will be held at the New Brunswick campus of Rutgers
University from Tuesday, July 12, through Friday, July 15, 1994.
The conference will be co-located with the Eleventh International
Conference on Machine Learning (ML 94), which will be held from
Sunday, July 10, through Wednesday, July 13. So the two
conferences overlap on Tuesday and Wednesday.
The COLT 94 conference is sponsored jointly by the ACM Special
Interest Groups for Algorithms and Computation Theory (SIGACT)
and Artificial Intelligence (SIGART).
We invite papers in all areas that relate directly to the
analysis of learning algorithms and the theory of machine
learning, including artificial and biological neural networks,
robotics, pattern recognition, inductive inference, information
theory, decision theory, Bayesian/MDL estimation, statistical
physics, and cryptography. We look forward to a lively,
interdisciplinary meeting. In particular we expect some fruitful
interaction between the research communities of the two
overlapping conferences. There will be a number of joint invited
talks. Prof. Michael Jordan from MIT will be one of the invited
speakers; the others will be announced at a later date.
Abstract Submission: Authors should submit twelve copies
(preferably two-sided copies) of an extended abstract to be
received by Thursday, February 3, 1994, to
Manfred Warmuth - COLT 94
225 Applied Sciences
Department of Computer Science
University of California
Santa Cruz, California 95064
An abstract must be received by February 3, 1994 (or
postmarked January 23 and sent airmail, or sent overnight
delivery on February 2). This deadline is FIRM! Papers that have
appeared in journals or other conferences, or that are being
submitted to other conferences, are not appropriate for
submission to COLT.
Abstract Format: The abstract should consist of a cover page with
title, authors' names, postal and e-mail addresses, and a 200-
word summary. The body of the abstract should be no longer than
10 pages with roughly 35 lines/page in 12-point font. Papers
deviating significantly from this length constraint will not be
considered. The body should include a clear definition of the
theoretical model used, an overview of the results, and some
discussion of their significance, including comparison to other
work. Proofs or proof sketches should be included in the
technical section. Experimental results are welcome, but are
expected to be supported by theoretical analysis.
Notification: Authors will be notified of acceptance or rejection
by a letter mailed on or before Monday, April 4, with possible
earlier notification via e-mail. Final camera-ready papers will
be due on Tuesday, May 3.
Program Format: Depending on submissions, and in order to
accommodate a broad variety of papers, the final program may
consist of both "long" talks, and "short" talks, corresponding to
longer and shorter papers in the proceedings. The short talks
will also be coupled with a poster presentation in special poster
sessions. By default, all papers will be considered for both
categories. Authors who do *not* want their papers considered
for the short category should indicate that fact in the cover
letter. The cover letter should also specify the contact author
and give his/her e-mail.
Program Chair: Manfred Warmuth (UC Santa Cruz, e-mail to
colt94@cse.ucsc.edu).
Conference and Local Arrangements Co-Chairs: Robert Schapire and
Michael Kearns (AT&T Bell Laboratories, e-mail to
colt94@research.att.com).
Program Committee: Shun'ichi Amari (U. Tokyo), Avrim Blum
(Carnegie Mellon), Nader Bshouty (U. Calgary), Bill Gasarch (U.
Maryland), Tom Hancock (Siemens), Michael Kearns (AT&T), Sara
Solla (Holmdel), Prasad Tadepalli (Oregon St. U.), Jeffrey
Vitter (Duke U.), Thomas Zeugmann (TU. Darmstadt).
------------------------------
Date: Mon, 4 Oct 93 15:29:22 EDT
From: Ashwin Ram <ashwin@cc.gatech.EDU>
Subject: CFP: AAAI-94 Spring Symposium on Goal-Driven Learning
CFP: AAAI-94 Spring Symposium on Goal-Driven Learning
AAAI-94 SPRING SYMPOSIUM ON
GOAL-DRIVEN LEARNING
Stanford University
March 21-23, 1994
CALL FOR PARTICIPATION
Program Committee:
Marie desJardins (co-chair) Lawrence Hunter
Foster John Provost Ashwin Ram (co-chair)
Goal-driven learning refers to the process of using the overall goals
of an intelligent system to make decisions about when learning should
occur, what should be learned, and which learning strategies are
appropriate in a given context. This focusing process may take place
at any decision point during learning---for example, when determining
what to learn, selecting a bias, pruning the space of theories to be
considered, or generating experiments for data gathering. Research in
psychology, education, and AI has shown the need for intelligent
systems to make decisions about what and how to learn. The common
rationale, and the principle around which the symposium will be
organized, is that the value of learning depends on how well it
satisfies the goals of the system. The symposium will bring together
researchers from diverse research areas to discuss issues in how
learning goals arise, how they affect learner decisions of when and
what to learn, and how they guide the learning process.
Topics addressed by the symposium will span the diverse work in this
area, which includes research in formulating learning goals,
experiment generation, utility of knowledge assessment, evaluating and
selecting learning biases, explanation-based learning, learning from
texts, active learning, case-based reasoning, formal analyses of
decision making, automated question generation, knowledge acquisition
planning, reinforcement learning, and control theory. We encourage
researchers from fields other than these to submit papers on related
research.
In addition to technical presentations, the symposium will include a
session of invited talks on relevant topics, such as formal utility
analyses of learning, automated experiment planning, psychological
evidence for goal-driven learning behavior in humans, human
educational motivation, and question generation. Depending on the
number and quality of submitted papers, we may include a poster
session as well. If there is sufficient overlap, we will arrange a
joint session with the symposium on Decision-Theoretic Planning.
Time will be set aside for debate and discussion during the technical
sessions, and the symposium will conclude with a panel and audience
discussion of the issues raised during the symposium. Members of the
concluding panel will be selected during the meeting, with the
intention of creating a panel representative of the viewpoints
expressed at the symposium.
In order to stimulate debate and discussion on future directions for
research as well as evaluation of existing approaches to goal-driven
learning, we encourage the submission of extended abstracts describing
work in progress, position papers, and papers describing innovative
unexplored approaches, as well as papers describing mature research
results.
If you wish to present a paper, please submit four hardcopies of a
paper or extended abstract by October 15 to:
Prof. Ashwin Ram
College of Computing
Georgia Institute of Technology
Atlanta, GA 30332-0280
(404) 853-9372
Shorter submissions (under five pages) are encouraged; however, longer
submissions (up to ten pages) will be accepted.
If you wish only to participate in the workshop, please submit two
hardcopies of a research summary describing your relevant research
interests.
Questions may directed to Ashwin Ram (ashwin@cc.gatech.edu) or to
Marie desJardins (marie@erg.sri.com).
SCHEDULE:
October 15, 1993 Papers due
November 15, 1993 Acceptance/rejection notices mailed
January 31, 1994 Camera-ready papers due
February 15, 1994 Registration deadline for invitees
March 1, 1994 Final registration deadline
March 21-23 Symposium at Stanford Univ.
------------------------------
Date: Fri, 1 Oct 93 17:11:43 EDT
From: salzberg@blaze.cs.jhu.EDU
Subject: PEBLS 2.0
__________________________________________________________
ANNOUNCEMENT
PEBLS 2.0 is now available via Anonymous FTP.
__________________________________________________________
PEBLS (Parallel Exemplar-Based Learning System) is a
nearest-neighbor learning system designed for applications
where the instances have symbolic feature values. PEBLS has
been applied to the prediction of protein secondary
structure based on the primary amino acid sequence of
protein sub-units, and to the identification of DNA promoter
sequences. A technical description appears in the article
by Cost and Salzberg, Machine Learning journal 10:1 (1993).
PEBLS 2.0 is a serial version written entirely in ANSI
C. PEBLS 2.0 incorporates a number of features intended
to support flexible experimentation in symbolic domains. We
have provided support for k-nearest neighbor learning, and
the ability to choose among different techniques for
weighting both exemplars and individual features. A number
of post-processing techniques specific to the domain of
protein secondary structure have also been provided.
TO OBTAIN PEBLS BY ANONYMOUS FTP
________________________________
The latest version of PEBLS is available free of charge, and
may be obtained via anonymous FTP from the Johns Hopkins
University Computer Science Department.
To obtain a copy of PEBLS, type the following commands:
UNIX_prompt> ftp blaze.cs.jhu.edu
[Note: the Internet address of blaze.cs.jhu.edu is 128.220.13.50]
Name: anonymous
Password: [enter your email address]
ftp> bin
ftp> cd pub/pebls
ftp> get pebls.tar.Z
ftp> bye
[Place the file pebls.tar.Z in a convenient subdirectory.]
UNIX_prompt> uncompress pebls.tar.Z
UNIX_prompt> tar -xf pebls.tar
[Read the files "README" and "pebls.doc"]
For further information, contact:
Prof. Steven Salzberg
Dept. of Computer Science
The Johns Hopkins University
Baltimore, MD 21210
Email: salzberg@cs.jhu.edu
PEBLS 2.0 IS INTENDED FOR RESEARCH PURPOSES ONLY. PEBLS 2.0 may be
used, copied, and modified for this purpose. Any commercial use of
PEBLS 2.0 is strictly prohibited without the express written consent
of Prof. Steven Salzberg, Department of Computer Science, The Johns
Hopkins University.
------------------------------
Date: Thu, 30 Sep 93 09:49 EDT
From: William Cohen <wcohen@research.att.COM>
Subject: ML '94 conference announcement (latex format)
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\ \hrulefill\
\\
\vspace*{5pt}
{\large\bf CALL FOR PAPERS---ML94}\\%
\vspace*{2pt}
{\LARGE\bf{Eleventh International Conference on Machine Learning}\\%
\vspace*{2pt}
\Large\bf{New Brunswick, New Jersey\\
July 10--13, 1994}\\%
}\ \hrulefill\
\\
\vspace*{7pt}
\end{center}
\end{minipage}%}
The Eleventh International Conference on Machine Learning (ML94) will
be held at the New Brunswick campus of Rutgers University during July
11--13, 1994, with workshops taking place on July 10th. The
conference will be co-located with the Seventh Annual Conference on
Computational Learning Theory (COLT94), which will be held July
12--15. We invite submissions to ML94 from researchers in machine
learning or related fields, such as psychology, statistics, or
neuroscience. The conference will include presentations of refereed
papers and invited talks.
\myheading{7}{Review Criteria}{2}
Each submitted paper will be reviewed by at least two members of the
program committee, and be judged on clarity, significance, and
originality. Submissions should contain new results that have not
been published previously. Submissions to ML94 may be submitted to
other conferences, but if so a statement to this effect must appear on
the title page.
\myheading{7}{Paper Format}{2}
Submissions must be clearly legible, with good quality print. Papers
are limited to a total of twelve (12) pages, excluding title page and
bibligraphy, but including all tables and figures. Papers must be
printed on 8 1/2" x 11" or A4 paper using 12 point type (10 characters
per inch for typewriters). Each page must have a maximum of 38 lines
and an average of 75 characters per line (corresponding to the
\LaTeX{} article style, 12 point). Papers not adhering to this format
may be returned without review. Double-sided printing is strongly
encouraged. The title page of each paper must include the e-mail and
postal addresses of all authors, an abstract, and one or more keywords
from the following list to aid in the reviewing process: analogy,
Bayesian learning, case-based reasoning, cognitive modeling,
computational learning theory, concept formation, decision trees,
discovery, explanation-based learning, genetic algorithms, inductive
logic programming, instance-based learning, knowledge acquisition,
minimum description length, neural networks, reformulation,
reinforcement learning, scientific theory formation, speedup learning,
theory refinement, unsupervised learning, constructive induction,
multi-strategy learning.
\myheading{7}{Requirements for Submission}{2}
Authors must submit five copies of their papers to the address below;
electronic or FAX submission is not acceptable. Papers must be
received by February 8, 1994. Notification of acceptance or rejection
will be mailed to the first (or designated) author by March 25, 1994.
Camera-ready copy of accepted papers will be due April 26, 1994. Send
conference paper submissions to: William W. Cohen, AT\&T Bell
Laboratories, 600 Mountain Avenue, Room 2A-427, Murray Hill, NJ 07974
(908)-582-2092.
\myheading{7}{Informal Workshops}{2}
Proposals are invited for informal workshops in areas of interest
related to machine learning. Send a two-page proposal to: Russell
Greiner, ML94 Workshop Chair, Siemens Corporate Research, 755 College
Road East, Princeton, NJ 08540, by December 1, 1993, indicating the
organizer(s), nature and objective of the proposed workshop, and the
likely number of attendees.
\end{minipage}~~~
\begin{small}
\begin{minipage}[t]{2in}
\begin{tabbing}
~\\[-0.5\baselineskip]
{\bf General Inquiries:}\\
ml94@cs.rutgers.edu\\
~\\[-0.5\baselineskip]
{\bf Conference Co-Chairs:}\\
William W. Cohen\\
AT\&T Bell Laboratories\\
600 Mountain Avenue\\
Murray Hill, NJ 07974\\
{\tt wcohen@research.att.com}\\
~\\[-0.75\baselineskip]
Haym Hirsh\\
Department of Computer Science\\
Rutgers University\\New Brunswick, NJ 08903\\
{\tt hirsh@cs.rutgers.edu}\\
~\\[-0.5\baselineskip]
{\bf Program Committee:}\\
David Aha, NRL \\
Yuichiro Anzai, Keio U. \\
Eric Baum, NEC \\
Francesco Bergadano, U. Torino \\
Wray Buntine, NASA\\
Jason Catlett, AT\&T \\
Marie des Jardins, SRI \\
Tom Dietterich, Oregon St. U. \\
Doug Fisher, Vanderbilt U. \\
John Grefenstette, NRL \\
Russ Greiner, Siemens \\
Geoff Hinton, U. Toronto \\
Leslie Kaelbling, Brown U. \\
Dennis Kibler, UCI \\
John Laird, U. Michigan \\
Sridhar Mahadevan, S. Florida \\
Hiroshi Motoda, Hitachi \\
Ray Mooney, U. Texas \\
Katharina Morik, U. Dortmund \\
Mike Pazzani, UCI \\
Lenny Pitt, U. Illinois \\
Lorien Pratt, Colorado S. of Mines \\
Armand Prieditis, UC/Davis \\
Paul Rosenbloom, USC/ISI \\
Stuart Russell, UCB \\
Lorenza Saitta, U. Torino \\
Claude Sammut, U. New S. Wales \\
Cullen Schaffer, Hunter College \\
Rich Sutton, GTE \\
Paul Utgoff, U. Mass \\
Stefan Wrobel, GMD \\
Steve Whitehead, GTE \\
Manuela Veloso, CMU \\
Kenji Yamanishi, NEC\\
~\\[-0.5\baselineskip]
{\bf Workshop Chair:}\\
Russell Greiner\\
Siemens Corporate Research\\
755 College Road East\\
Princeton, NJ 08540\\
{\tt greiner@learning.scr.siemens.com}\\
~\\[-0.5\baselineskip]
{\bf Local Arrangements:}\\
Priscilla Rasmussen\\
Department of Computer Science\\
Rutgers University\\
New Brunswick, NJ 08903\\
{\tt rasmusse@cs.rutgers.edu}
\end{tabbing}
\end{minipage}\end{small}
\end{document}
------------------------------
End of ML-LIST (Digest format)
****************************************
