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Machine Learning List Vol. 5 No. 12
Machine Learning List: Vol. 5 No. 12
Monday, May 31, 1993
Contents:
Machine Learning 11:1
ECML
Call for Proposals, ML95
COLT'93 Program and Registration Form
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
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----------------------------------------------------------------------
From: Tom Dietterich <tgd@chert.CS.ORST.EDU>
To: ml@ics.uci.edu
Subject: Machine Learning 11:1
Machine Learning
April 1993, Volume 11, Number 1
Coding Decision Trees
C. S. Wallace and J. D. Patrick
Active Learning Using Arbitrary Binary Valued Queries
S. R. Kulkarni, S. K. Mitter, and J. N. Tsitsiklis
Noise-Tolerant occam Algorithms and Their Applications to Learning
Decision Trees
Y. Sakakibara
Very Simple Classification Rules Perform Well on Most Commonly Used
Datasets
R. C. Holte
An Analysis of the WITT Algorithm
J. L. Talmon, P. J. Braspenning, and H. Fonteijn
-----
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Hingham, MA 02018-0358 USA THE NETHERLANDS
------------------------------
Date: Sat, 29 May 93 17:35:02 PDT
From: Wray Buntine <wray@ptolemy.arc.nasa.GOV>
Subject: ECML CFP
First Call for Papers of the
7th EUROPEAN CONFERENCE ON MACHINE LEARNING (ECML94)
The 7th ECML will be organized in Catania, Sicily (Italy), from April
6 to April 8 1994. The 7th ECML solicits papers in all areas of
Machine Learning, including, but not limited to :
analogy
applications of machine learning
case based reasoning
computational learning theory
automated discovery
explanation based learning
inductive learning
inductive logic programming
genetic algorithms
learning and problem solving
multistrategy learning
neural networks
representation change
Full papers are limited to 5000 words. Submissions should be made in 5
copies and should be received by the program chairmen on or before 15
October 1993 at the following address (notification by 15 December):
Luc De Raedt - Francesco Bergadano (ECML-94)
Department of Computing Science, Katholieke Universiteit Leuven
Celestijnenlaan 200A, B-3001 Heverlee (Belgium)
To receive further information about ECML 94, send email to
ecml@cs.kuleuven.ac.be.
Program Chairs:
Francesco Bergadano (University of Catania, Italy) and
Luc De Raedt (Katholieke Universiteit Leuven, Belgium).
Program Committee:
Ivan Bratko (Slovenia) Pavel Brazdil (Portugal),
Wray Buntine (USA) Floriana Esposito (Italy),
Jean-Gabriel Ganascia (France) Igor Kononenko (Slovenia),
Yves Kodratoff (France) Nada Lavrac (Slovenia),
Stan Matwin (Canada) Katharina Morik (Germany),
Igor Mozetic (Austria) Stephen Muggleton (UK),
Enric Plaza (Spain) Lorenza Saitta (Italy),
Derek Sleeman (UK) Paul Vitanyi (Netherlands),
Gerhard Widmer (Austria) Stefan Wrobel (Germany).
------------------------------
Subject: Call for Proposals, ML95
From: Tom Dietterich <tgd@chert.CS.ORST.EDU>
Date: Fri, 28 May 93 09:32:09 PDT
1995 INTERNATIONAL MACHINE LEARNING CONFERENCE
CALL FOR PROPOSALS
In June 1995, the Twelfth International Machine Learning Conference will
be held. The purpose of this call is to invite groups interested in
organizing and hosting the conference to submit proposals. The group
selected to run the conference will be given full authority and
responsibility for producing the conference.
Proposals should address the following issues:
1. Organization and Format. In previous years, the format of the
conference has alternated annually between a single plenary session
(1988, 1990) and a set of parallel workshop sessions (1989, 1991).
However, in 1992, 1993, and 1994, the format involved 3 days of plenary
sessions followed by one day of specialized workshops. A poster
session has also been held at each conference. Please indicate what
format you would like to have and how you would arrange the schedule
to suit the format. You may present more than one possible format, if
you wish.
In the past, the conference has been organized by an Organizing
Committee whose membership included organizers of past conferences and
other senior researchers. Review of papers has been conducted by a
Program Committee selected by the organizers with the advice of the
Organizing Committee. Please indicate what organization you would
employ.
2. Locale Parameters.
- Accessibility. Is it easy and inexpensive for people (especially
graduate students) to travel to the conference site? (Compute
mean airfares from Europe and North America.)
- Meeting Rooms, AV Equiment, etc. What are the physical
facilities like?
- Meals and Lodging. Is there low-cost, quality housing available
for attendees (especially graduate students)? How far from the
meeting rooms? Where will attendees eat?
- Demo facilities. Will there be computing equipment and space
available to support demos?
3. Local Machine Learning Community. Is there a local ML
group/community that can help with organization and funding?
4. Organizational and Financial Support. Can the host institution(s)
provide support for registration and financial management (e.g.,
credit card payments, accounting, etc.). How will the conference be
funded? Provide a draft budget covering expenses, expected
registration fee schedule, and sources of financial support. The host
institution must agree to forward any unused funds to the host of the
1996 conference. In previous years, funding has been obtained from
federal granting agencies, corporations, and universities.
Proposals should be sent before June 24, 1993 to
Tom Dietterich
Department of Computer Science
303 Dearborn Hall
Oregon State University
Corvallis, OR 97331-3202
tgd@cs.orst.edu
The choice of organizers will be made at the June meeting of the
Editorial Board of the Machine Learning Journal, which is tentatively
scheduled for 3:00pm, June 28 during the 1993 International Machine
Learning Conference.
------------------------------
Date: Thu, 20 May 93 19:21:01 -0400
From: Ming Li <mli@math.uwaterloo.ca>
Subject: COLT'93 Program and Registration Form
** COLT mailing list message **
COLT '93
Sixth ACM Conference on Computational Learning Theory
Monday, July 26 through Wednesday, July 28, 1993
University of California, Santa Cruz, California
The workshop will be held on campus, which is hidden away in the
redwoods on the Pacific coast of Northern California. The workshop is
sponsored by the ACM Special Interest Group on Algorithms and Computation
Theory (SIGACT) and the ACM Special Interest Group on Artificial
Intelligence (SIGART). The long version of this document is available
by anonymous ftp from ftp.cse.ucsc.edu. To ftp the document you
do the following: step 1) ftp ftp.cse.ucsc.edu, and login as "anonymous",
2) cd pub/colt, 3) binary, 4) get colt93.registration.ps.
REGISTRATION INFORMATION
Please fill in the information needed on the registration sheet
Make your payment by check or international money order,
in U.S. dollars and payable through a U.S. bank, to COLT '93.
Mail the form together with payment (by June 15 to avoid the late fee) to:
COLT '93
Dept. of Computer Science
University of California
Santa Cruz, California 95064
ACCOMMODATIONS AND DINING
Accommodation fees are $57 per person for a double and $70 for a single
per night at the College Eight Apartments. Cafeteria style breakfast
(7:45 to 8:30am), lunch (12:30 to 1:30pm), and dinner (6:00 to 7:00pm)
will be served in the College Eight Dining Hall. Doors close at the
end of the time indicated, but dining may continue beyond this time.
The first meal provided is dinner on the day of arrival and the last
meal is lunch on the day you leave. NO REFUNDS can be given after June 15.
Those with uncertain plans should make reservations at an off-campus hotel.
Each attendee should pick one of the five accomdation packages.
For shorter stays, longer stays, and other special requirements, you can get
other accommodations through the Conference Office. Make reservations
directly with them at (408)459-2611, fax (408)459-3422, and do this soon
as on-campus rooms for the summer fill up well in advance. Off-campus
hotels include the Dream Inn (408)426-4330 and the Holiday Inn (408)426-7100.
Questions: e-mail colt93@cse.ucsc.edu, fax (408)429-4829.
Confirmations will be sent by e-mail. Anyone needing special arrangements
to accommodate a disability should enclose a note with their registration.
If you don't receive confirmation within three weeks of payment, let us know.
Get updated versions of this document by anonymous ftp from
ftp.cse.ucsc.edu.
CONFERENCE REGISTRATION FORM (see accompanying information for details)
Name: ___________________________________
Affiliation: ___________________________________
Address: ___________________________________
City: ________________ State: ____________ Zip: ________________
Country: ____________________
Telephone: (____) ________________
Email: ________________________
The registration fee includes a copy of the proceedings.
ACM/SIG Members: $165 (with banquet) $___________
Non-Members: $185 (with banquet) $___________
Late: $220 (postmarked after June 15) $___________
Full time students: $80 (no banquet) $___________
Extra banquet tickets: ___ (quantity) x $18 = $___________
How many in your party have dietary restrictions?
Vegetarian: ___________ Other: ___________
Shirt size, please circle one: small medium large x-large
ACCOMODATIONS: Pick one package:
_____ Package 1: Sun, Mon, Tue nights: $171 double, $210 single.
_____ Package 2: Sat, Sun, Mon, Tue nights: $228 double, $280 single.
_____ Package 3: Sun, Mon, Tues, Wed nights: $228 double, $280 single.
_____ Package 4: Sat, Sun, Mon, Tue, Wed nights: $285 double, $350 single.
______Other housing arrangement.
Each 4-person apartment has a living room, a kitchen, two common bathrooms,
and either four single separate rooms, two double rooms, or two single and
one double room. We need the following information to make room assignments:
Gender (M/F): __________ Smoker (Y/N): __________
Roommate Preference: ____________________
AMOUNT ENCLOSED:
Registration $___________________
Banquet tickets $___________________
Accommodations $___________________
TOTAL $___________________
Mail this form together with payment (by June 15 to avoid the late fee) to:
COLT '93, Dept. of Computer Science, Univ. California, Santa Cruz, CA 95064
COLT '93 Conference Schedule
Sixth ACM Conference on Computational Learning Theory
Monday, July 26 through Wednesday, July 28, 1993
University of California, Santa Cruz, California
SUNDAY, JULY 25
4:00 - 6:00 pm, Housing Registration, College Eight Satellite Office.
7:00 - 10:00 pm, Reception, Oakes Learning Center.
Preregistered attendees may check in at the reception.
Note: All technical sessions will take place in Oakes 105 .
MONDAY, JULY 26
Session 1: Learning with Queries
Chair: Dana Angluin
8:20-8:40
Learning Sparse Polynomials over Fields with Queries and Counterexamples.
Robert E. Schapire and Linda M. Sellie
8:40-9:00
Learning Branching Programs with Queries.
Vijay Raghavan and Dawn Wilkins
9:00-9:10
Linear Time Deterministic Learning of k-term DNF.
Ulf Berggren
9:10-9:30
Asking Questions to Minimize Errors.
Nader H. Bshouty, Sally A. Goldman, Thomas R. Hancock, and Sleiman Matar
9:30-9:40
Parameterized Learning Complexity.
Rodney G. Downey, Patricia Evans, and Michael R. Fellows
9:40-10:00
On the Query Complexity of Learning.
Sampath K. Kannan
10:00 - 10:30 BREAK
Session 2: New Learning Models and Problems
Chair: Sally Goldman
10:30-10:50
Teaching a Smarter Learner.
Sally A. Goldman and H. David Mathias
10:50-11:00
Learning and Robust Learning of Product Distributions.
Klaus-U. Hoffgen
11:00-11:20
A Model of Sequence Extrapolation.
Philip Laird, Ronald Saul and Peter Dunning
11:20-11:30
On Polynomial-Time Probably Almost Discriminative Learnability.
Kenji Yamanishi
11:30-11:50
Learning from a Population of Hypotheses.
Michael Kearns and Sebastian Seung
11:50-12:00
On Probably Correct Classification of Concepts.
S.R. Kulkarni and O. Zeitouni
12:00 - 1:40 LUNCH
Session 3: Inductive Inference; Neural Nets
Chair: Bob Daley
1:40-2:00
On the Structure of Degrees of Inferability.
Martin Kummer and Frank Stephan
2:00-2:20
Language Learning in Dependence on the Space of Hypotheses.
Steffen Lange and Thomas Zeugmann
2:20-2:30
On the Power of Sigmoid Neural Networks.
Joe Kilian and Hava T. Siegelmann
2:30-2:40
Lower Bounds on the Vapnik-Chervonenkis Dimension of
Multi-layer Threshold Networks.
Peter L. Bartlett
2:40-2:50
Average Case Analysis of the Clipped Hebb Rule
for Nonoverlapping Perceptron Networks.
Mostefa Golea and Mario Marchand
2:50-3:00
On the Power of Polynomial Discriminators and Radial Basis
Function Networks.
Martin Anthony and Sean B. Holden
3:00 - 3:30 BREAK
3:30-4:30 Invited Talk by Geoffrey Hinton
The Minimum Description Length Principle and Neural Networks.
4:45 - ? Impromptu talks, open problems, etc.
7:00 - 10:00 pm, Banquet, barbeque pit outside Porter Dining Hall.
TUESDAY, JULY 27
Session 4: Inductive Inference
Chair: Rolf Wiehagen
8:20-8:40
The Impact of Forgetting on Learning Machines.
Rusins Freivalds, Efim Kinber, and Carl H. Smith
8:40-8:50
On Parallel Learning.
Efim Kinber, Carl H. Smith, Mahendran Velauthapillai, and Rolf Wiehagen
8:50-9:10
Capabilities of Probabilistic Learners with Bounded Mind Changes.
Robert Daley and Bala Kalyanasundaram
9:10-9:20
Probability is More Powerful than Team for Language
Identification from Positive Data.
Sanjay Jain and Arun Sharma
9:20-9:40
Capabilities of Fallible FINite Learning.
Robert Daley, Bala Kalyanasundaram, and Mahendran Velauthapillai
9:40-9:50
On Learning in the Limit and Non-uniform (epsilon, delta)-Learning.
Shai Ben-David and Michal Jacovi
9:50 - 10:20 BREAK
Session 5: Formal Languages, Rectangles, and Noise
Chair: Takeshi Shinohara
10:20-10:40
Learning Fallible Deterministic Finite Automata.
Dana Ron and Ronitt Rubinfeld
10:40-11:00
Learning Two-Tape Automata from Queries and Counterexamples.
Takashi Yokomori
11:00-11:10
Efficient Identification of Regular Expressions from Representative
Examples. Alvis Brazma
11:10-11:30
Learning Unions of Two Rectangles in the Plane with Equivalence Queries.
Zhixiang Chen
11:30-11:50
On-line Learning of Rectangles in Noisy Environments.
Peter Auer
11:50-12:00
Statistical Queries and Faulty PAC Oracles.
Scott Evan Decatur
12:00 - 1:40 LUNCH
Session 6: New Models; Linear Thresholds
Chair: Wray Buntine
1:40-2:00
Learning an Unknown Randomized Algorithm from its Behavior.
William Evans, Sridhar Rajagopalan, and Umesh Vazirani
2:00-2:20
Piecemeal Learning of an Unknown Environment.
Margrit Betke, Ronald L. Rivest, and Mona Singh
2:20-2:40
Learning with Restricted Focus of Attention.
Shai Ben-David and Eli Dichterman
2:40-2:50
Polynomial Learnability of Linear Threshold Approximations.
Tom Bylander
2:50-3:00
Rate of Approximation Results Motivated by Robust Neural Network Learning.
Christian Darken, Michael Donahue, Leonid Gurvits, and Eduardo Sontag
3:00-3:10
On the Average Tractability of Binary Integer Programming and the
Curious Transition to Generalization in Learning Majority Functions.
Shao C. Fang and Santosh S. Venkatesh
3:10 - 3:30 BREAK
3:30-4:30 Invited Talk by John Grefenstette
Genetic Algorithms and Machine Learning
4:45 - ? Impromptu talks, open problems, etc.
7:00 - 8:30 Poster Session and Dessert
Oakes Learning Center
8:30 - 10:00 Business Meeting
Oakes 105
WEDNESDAY, JULY 28
Session 7: Pac Learning
Chair: Yishay Mansour
8:20-8:40
On Learning Visual Concepts and DNF Formulae.
Eyal Kushilevitz and Dan Roth
8:40-9:00
Localization vs. Identification of Semi-Algebraic Sets.
Shai Ben-David and Michael Lindenbaum
9:00-9:20
On Learning Embedded Symmetric Concepts.
Avrim Blum, Prasad Chalasani, and Jeffrey Jackson
9:20-9:30
Amplification of Weak Learning Under the Uniform Distribution.
Dan Boneh and Richard J. Lipton
9:30-9:50
Learning Decision Trees on the Uniform Distribution.
Thomas R. Hancock
9:50 - 10:20 BREAK
Session 8: VC dimension, Learning Complexity, and Lower Bounds
Chair: Sebastian Seung
10:20-10:40
Bounding the Vapnik-Chervonenkis Dimension of Concept Classes
Parameterized by Real Numbers.
Paul Goldberg and Mark Jerrum
10:40-10:50
Occam's Razor for Functions.
B.K. Natarajan
10:50-11:00
Conservativeness and Monotonicity for Learning Algorithms.
Eiji Takimoto and Akira Maruoka
11:00-11:20
Lower Bounds for PAC Learning with Queries.
Gyorgy Turan
11:20-11:40
On the Complexity of Function Learning.
Peter Auer, Philip M. Long, Wolfgang Maass, and Gerhard J. Woeginger
11:40-12:00
General Bounds on the Number of Examples Needed for Learning
Probabilistic Concepts.
Hans Ulrich Simon
NOON: Check-out of Rooms
12:00 - 1:40 LUNCH
Session 9: On-Line Learning
Chair: Kenji Yamanishi
1:40-2:00
On-line Learning with Linear Loss Constraints.
Nick Littlestone and Philip M. Long
2:00-2:10
The `Lob-Pass' Problem and an On-line Learning Model of Rational Choice.
Naoki Abe and Jun-ichi Takeuchi
2:10-2:30
Worst-case Quadratic Loss Bounds for a Generalization of the
Widrow-Hoff Rule.
Nicolo Cesa-Bianchi, Philip M. Long, and Manfred K. Warmuth
2:30-2:40
On-line Learning of Functions of Bounded Variation under
Various Sampling Schemes.
S.E. Posner and S.R. Kulkarni
2:40-2:50
Acceleration of Learning in Binary Choice Problems.
Yoshiyuki Kabashima and Shigeru Shinomoto
2:50-3:10
Learning Binary Relations Using Weighted Majority Voting.
Sally A. Goldman and Manfred K. Warmuth
3:10 CONFERENCE ENDS
3:10 - ? Last fling on the Boardwalk.
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End of ML-LIST (Digest format)
****************************************
