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Machine Learning List Vol. 6 No. 05
Machine Learning List: Vol. 6 No. 5
Wednesday, February 23, 1994
Contents:
Job posting- GTE
ECAI'94 KA&ML workshop
Learning with missing and noisy features
Intelligent Systems for Molecular Biology Conference
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Date: Tue, 15 Feb 94 14:40:19 EST
From: Bernard Silver <bernard%sirius@gte.com>
Subject: Job posting- GTE
GTE Laboratories Incorporated
Waltham MA
GTE Laboratories is the corporate R&D center for GTE. The Adaptive
Systems Department is seeking an experienced researcher for R&D
working on a Diagnostic KBS for a telecommunications application. In
the near term, the job will involve design, development and deployment
of a robust field prototype. The system will have to diagnose
problems in future technology network components. There is little
existing expertise in these devices so we expect significant use of
Machine Learning and other AI methods.
Longer term, the employee will be responsible for exploring possible
applications of technologies in the control of telecommunications
networks or in telecommunications-related services, the development of
domain-specific applications, and the transfer of those applications
to specific GTE business units. The employee will interact with the
business units to determine appropriate application domains, and will
interface to other research groups in GTE Labs that are providing
technologies for telecommunications network operations, administration
and maintenance.
Occasional travel to GTE sites will be required.
Education/Experience
PhD in Artifical Intelligence or related field; Machine Learning and
post-PhD industrial experience preferred. Need significant
programming skills, Lisp or Prolog required, ART-IM/CLIPS a big plus,
C helpful. Previous experience in Diagnostic KBS desirable.
GTE Laboratories is located in Waltham, MA, about 12 miles from Boston.
For more information contact:
Bernard Silver
GTE Labs, MS 44
Waltham MA 02254
(617) 466-2663
bsilver@gte.com
------------------------------
Date: Mon, 14 Feb 94 14:02:26 +0100
From: Claire.Nedellec@lri.fr
Subject: ECAI'94 KA&ML workshop
Call for papers / Call for participation
ECAI'94 (11th European Conference on AI) Workshop
Integration of Machine Learning and Knowledge Acquisition
Amsterdam, The Netherlands, August 8, 1994
DESCRIPTION
We propose a follow-up to the successful workshop organized at
IJCAI'93. This workshop has illustrated the growing interest in this
topic. Contrary to the naive thinking that the integration of ML and
KA should be obvious "since ML provides KA reusability" and "KA
provides ML knowledge of the domain", it seems that their integration
actually demands progress in both fields, as many papers of the IJCAI
workshop have shown. Some promising research directions have arisen,
that we will discuss during this wokshop. A non-exhaustive list of
topics is :
Specification of the required input of ML systems. (What is
provided by the user.)
Characterisation of the output of ML systems in user's goal
terms. (What is the ML task.)
Elicitation of ``hidden'' control knowledge of ML
systems. (For example, what are the effects of the user's
representation choices, What is a ``good'' example set.)
Interaction with the user. (Why and when.)
Modelling ML systems at the knowledge level.
How can ML systems help to implement general knowledge
acquisition methodologies.
Reusability of KA tools in ML framework.
A strict selection criterion for acceptation to the workshop will be
that the interactions between the two techniques are really studied.
Authors are encouraged to report on their practical experience on
development of real applications of ML that require a KA phase and
applications of KA that need the use of ML.
SUBMISSION REQUIREMENTS:
Authors should submit a preliminary paper (up to 20 pages) fully
explaining the relevance of their work to the workshop. Persons
wishing to participate but who do not wish to give a presentation
should submit an abstract (1 page) describing their research and/or
interest in the subject area and their expected contributions to the
workshop. Preliminary papers and abstracts should be submitted in five
copies by April 15 to:
Claire Nedellec
LRI Bat 490
Universite Paris-Sud
F-91405 Orsay cedex France
Tel : +33 (1) 69 41 64 62
Fax : +33 (1) 69 41 65 86
Email submissions (in latex or RTF format) accepted at cn@lri.fr
Authors will receive notification of acceptance by May 20, and final
papers will be due by June 10, 1994.
ORGANIZATION
All attendees will receive before the workshop a list of topics and
open questions that have emerged from the papers to be presented. To
stimulate discussions, presentations will have to refer to these. The
workshop will start with a panel presenting the key issues discussed
during the sessions. Schedule will give time for open discussions and
syntheses.
Organizing Comittee
Yves Kodratoff, (Universite de Paris-Sud, Orsay, France),
Claire Nedellec, (Universite de Paris-Sud, Orsay, France).
Program Comitte
A. Aamodt, (Univ. of Trondheim, Norway)
T. Addis, (Univ. of Reading, UK)
P. Albert, (ILOG, France)
S. Arikawa, (Univ. of Kyushu, Japan)
P. Brazdil, (Univ. of Porto, Portugal)
D. Canamero, (Univ. of Paris-Sud, France)
K. Causse (Univ. of Paris-Sud, France)
F. Esposito, (Univ. of Bari, Italy)
R. Feldman, (Univ. of Bar Ilan, Israel)
D. Fensel (Univ. Karlsruhe, Germany)
J.G. Ganascia, (Univ. of Paris VI, France)
Y. Gil, (Information Sciences Institute, USA)
J. Herrmann, (Univ. of Dortmund, Germany)
S. Kedar (ILS, Northwestern University, USA)
N. Lavrac, (Jozef Stefan Institute, Slovenia)
R. Lopez de Mantaras, (IIIA-CSIC, Spain)
I. Mozetic (Tech. Univ. of Vienna & ARIAI, Austria)
E. Plaza, (IIIA-CSIC, Spain)
C. Rouveirol, (Univ. of Paris-Sud, France)
F. Schmalhofer, (DFKI Kaiserlautern, Germany)
G. Tecuci, (George Mason Univ., USA & Romanian Ac.)
M. van Someren, (Univ. of Amsterdam, The Netherlands)
------------------------------
Date: Mon, 14 Feb 94 10:56:49 -0800
From: ahmad@interval.com
Subject: Learning with missing and noisy features
The following paper is available for anonymous ftp on
archive.cis.ohio-state.edu (128.146.8.52), in directory
pub/neuroprose, as file "tresp.deficient.ps.Z". (The companion paper,
"Some Solutions to the Missing Feature Problem in Vision" is available
as "ahmad.missing.ps.Z")
Training Neural Networks with Deficient Data
Volker Tresp Subutai Ahmad
Siemens AG Interval Research Corporation
Central Research 1801-C Page Mill Rd.
81730 Muenchen, Germany Palo Alto, CA 94304
tresp@zfe.siemens.de ahmad@interval.com
Ralph Neuneier
Siemens AG
Central Research
Otto-Hahn-Ring 6
81730 Muenchen, Germany
ralph@zfe.siemens.de
Abstract:
We analyze how data with uncertain or missing input features can be
incorporated into the training of a neural network. The general
solution requires a weighted integration over the unknown or uncertain
input although computationally cheaper closed-form solutions can be
found for certain Gaussian Basis Function (GBF) networks. We also
discuss cases in which heuristical solutions such as substituting the
mean of an unknown input can be harmful.
The paper will appear in:
Cowan, J.D., Tesauro, G., and Alspector, J. (Eds.), Advances in
Neural Information Processing Systems 6. San Francisco CA: Morgan
Kaufmann, 1994.
Subutai Ahmad
Interval Research Corporation Phone: 415-354-3639
1801-C Page Mill Rd. Fax: 415-354-0872
Palo Alto, CA 94304 E-mail: ahmad@interval.com
------------------------------
From: brutlag@cmgm.stanford.edu (Doug Brutlag)
Subject: Intelligent Systems for Molecular Biology Conference
Date: Thu, 17 Feb 94 08:58:02 GMT
The Second International Conference on
Intelligent Systems for Molecular Biology
August 15-17, 1994
Stanford University
Organizing Committee Deadlines
Russ Altman, Stanford U, Stanford Papers due: March 11, 1994
Doug Brutlag, Stanford U, Stanford Replies to authors: April 29, 1994
Peter Karp, SRI, Menlo Park Revised papers due: May 27, 1994
Richard Lathrop, MIT, Cambridge
David Searls, U Penn, Philadelphia
Program Committee
K. Asai, ETL, Tsukuba A. Lapedes, LANL, Los Alamos
D. Benson, NCBI, Bethesda M. Mavrovouniotis, Northwestern U, Evanston
B. Buchanan, U of Pittsburgh G. Michaels, George Mason U, Fairfax
C. Burks, LANL, Los Alamos G. Myers, U. Arizona, Tucson
D. Clark, ICRF, London K. Nitta, ICOT, Tokyo
F. Cohen, UCSF, San Francisco C. Rawlings, ICRF, London
T. Dietterich, OSU, Corvallis J. Sallatin, LIRM, Montpellier
S. Forrest, UNM, Albuquerque C. Sander, EMBL, Heidelberg
J. Glasgow, Queen's U., Kingston J. Shavlik, U Wisconsin, Madison
P. Green, Wash U, St. Louis D. States, Wash U, St. Louis
M. Gribskov, SDSC, San Diego G. Stormo, U Colorado, Boulder
D. Haussler, UCSC, Santa Cruz E. Uberbacher, ORNL, Oak Ridge
S. Henikoff, FHRC, Seattle M. Walker, Stanford U, Stanford
L. Hunter, NLM, Bethesda T. Webster, Stanford U, Stanford
T. Klein, UCSF, San Francisco X. Zhang, TMC, Cambridge
The Second International Conference on Intelligent Systems for Molecular
Biology will take place at Stanford University in the San Francisco Bay
Area, August 14-17, 1994. The ISMB conference, held for the first time
last summer in Bethesda, MD, attracted an overflow crowd, yielded an
excellent offering of papers, invited speakers, posters and tutorials,
provided an exciting opportunity for researchers to meet and exchange
ideas, and was an important forum for the developing field. We will
continue the tradition of pre-published, rigorously refereed proceedings,
and opportunities for fruitful personal interchange.
The conference will bring together scientists who are applying the
technologies of advanced data modeling, artificial intelligence, machine
learning, probabilistic reasoning, massively parallel computing, robotics,
and related computational methods to problems in molecular biology. We
invite participation from both developers and users of any novel system,
provided it supports a biological task that is cognitively challenging,
involves a synthesis of information from multiple sources at multiple
levels, or in some other way exhibits the abstraction and emergent
properties of an "intelligent system." The four-day conference will
feature introductory tutorials (August 14), presentations of original
refereed papers and invited talks (August 15-17).
Paper submissions should be single-spaced, 12 point type, 12 pages
maximum including title, abstract, figures, tables, and bibliography with
titles. The first page should include the full postal address, electronic
mailing address, telephone and FAX number of each author. Also, please
list five to ten keywords describing the methods and concepts discussed
in the paper. State whether you wish the paper to be considered for oral
presentation only, poster presentation only or for either presentation
format. Submit 6 copies to the address below. For more information,
please contact ismb@camis.stanford.edu.
Proposals for introductory tutorials must be well documented, including
the purpose and intended audience of the tutorial as well as previous
experience of the author in presenting such material. Those considering
submitting tutorial proposals are strongly encouraged to submit a one-page
outline, before the deadline, to enable early feed-back regarding topic
and content suitability. The conference will pay an honorarium and
support, in part, the travel expenses of tutorial speakers.
Limited funds are available to support travel to ISMB-94 for those students,
post-docs, minorities and women who would otherwise be unable to attend..
Please submit papers and tutorial proposals to:
Intelligent Systems for Molecular Biology
c/o Dr. Douglas L. Brutlag
Beckman Center, B400
Department of Biochemistry
Stanford University School of Medicine
Stanford, California 94305-5307
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End of ML-LIST (Digest format)
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