--
Discovery community, focusing on the latest research and applications.
Contributions are most welcome and should be emailed,
with a DESCRIPTIVE subject line (and a URL, when available) to (kdd@gte.com).
E-mail add/delete requests to (kdd-request@gte.com).
Nuggets frequency is approximately weekly.
Back issues of Nuggets, a catalog of S*i*ftware (data mining tools),
and a wealth of other information on Data Mining and Knowledge Discovery
is available at Knowledge Discovery Mine site, URL http://info.gte.com/~kdd.
-- Gregory Piatetsky-Shapiro (moderator)
********************* Official disclaimer ***********************************
* All opinions expressed herein are those of the writers (or the moderator) *
* and not necessarily of their respective employers (or GTE Laboratories) *
*****************************************************************************
~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
We cannot predict for sure where the Computer Revolution will take us.
All we know for certain is when we finally get there,
we will not have enough RAM
Dave Barry
(see excerpts from Dave Barry recent and very funny book
on computers in http://www.yil.com/yil/dbarry/index.html Previous1NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Thu, 19 Sep 1996 09:22:56 -0400
From: gps@gte.com
(Gregory Piatetsky-Shapiro)
Subject: [moneyadm@pathfinder.com:
MONEY Daily: Privacy storm shows the best and worst of the 'Net]
I am enclosing an article from 'MONEY Daily' on privacy issues.
Date: Wed, 18 Sep 1996 21:44:36 EST
From: moneyadm@pathfinder.com
(MONEY Daily)
To: 'MONEY Daily summary of news affecting your finances' (moneydaily@relay.pathfinder.com)
Subject: MONEY Daily: Privacy storm shows the best and worst of the 'Net
X-Listprocessor-Version: 8.0 -- ListProcessor(tm) by CREN
Thursday, September 19, 1996
Privacy storm shows the best and worst of the 'Net
The flap over a new service from Lexis-Nexis is probably
misguided, but raises interesting questions about privacy and
the power of online communication
by Michael Brush
An online brouhaha that broke out this week over alleged
privacy breaches caused by a new Lexis-Nexis product has put
both the best and worst of the 'Net community in high
profile.
The Dayton, Ohio-based online database company has been
swamped with phone calls and faxes this week because of a
flurry of e-mails and news-group postings warning readers of
the allegedly dire consequences of its new product called P-
TRAK. Many of the warnings falsely claim that P-TRAK provides
a wide range of potentially sensitive personal information
ranging from social security numbers to medical and credit
histories.
On the bright side, the controversy shows the huge power of
the 'Net when used by public-spirited advocates to mobilize a
response to a perceived danger -- in this case, a threat to
privacy. On the dark side, the fact that many of the
allegations in the electronic correspondence are plain wrong
shows how easy it is to spread misinformation and,
potentially, raise what could amount to an online lynch mob.
At issue is a product launched last June which provides
Nexis-Lexis clients with basic public information about
anyone in a 300 million-name database. The information
available is culled from credit bureau records. It is limited
to your name, your maiden name or alias (if any), your
current and two most recent past addresses, the month and
year of your birth, and your phone number -- though not all
of that information is available on every person.
For nine days after the product was released last June 2, P-
TRAK also included your social security number. But the SSNs
were pulled on June 11 because of complaints, says Lexis-
Nexis spokesman Steve Edwards. If you already know
someone's social security number, though, you can search for
him or her using that number.
P-TRAK is meant to help attorneys track down witnesses, heirs
or parents who have stopped paying child support, says
Edwards.
While the scope of P-TRAK is relatively limited compared to,
say, credit reports, which carry news of your payment
history, bankruptcies and other such sensitive material, it
nonetheless sparked the ire of many 'Net users.
For example, one posting that was later widely circulated
this week claimed that your social security number, 'mother's
maiden name, birth date and other personal information are
now available to anyone with a credit card.' It postulated
that the information could allow someone to commit credit
card fraud or use your identity.
The truth is somewhat tamer. The SSN and mother's maiden name
are not included, for example, and the database is available
only to clients like law firms and news organizations that
can afford Lexis-Nexis's relatively hefty fees.
Furthermore, Money Daily's spot check shows that the
information available is spotty at best. A search of P-TRAK
run on our behalf by the Time Inc. Research Center turned up
the right addresses but no phone or birth date for this
author. It got the addresses and birth year correct for Money
Daily editor Kevin McKean, but had an obsolete phone number.
And when confronted with relatively more common names of two
other Money Online staffers -- tech director Wilson Smith and
reporter Joseph 'Tripp' Reynolds -- turned up dozens of
people, none of whom proved to be the correct ones.
Those limitations notwithstanding, the roar on the 'Net was
heard at Lexis-Nexis headquarters. 'We have been deluged with
people calling, writing and faxing,' says Edwards. 'People
are asking us: 'Why are you putting my medical records, my
mother's maiden name, and my credit card history out there?'
This has been testing the limits of our customer service.'
In response, Lexis-Nexis posted a statement on its home
page to correct false information about the product and
also a form you can use to remove your name from the list,
something the company says people could have done all along
by calling. By next Monday, Edwards says the firm will also
have an 800 number that callers can use. People who phone the
company's current 800 number are told to fax name-removal
requests to 513-865-1930.
The company has stopped short of posting responses in news
groups or bulletin boards, though. 'There are different
schools of thought on that,' says Edwards. 'One says that you
should never respond to news groups from a company standpoint
because that increases the amount of flaming. But at this
point, I don't know how much worse it could get.'
In the company's place, other news group participants have
stepped in to set the record straight. 'I think you owe a
post to correct this error,' one such correspondent scolded
in a reply that pointed out several errors in the message
quoted above.
Ironically, the information available in P-TRAK is mild
compared to what is available elsewhere in the vast Lexis-
Nexis database.
A related product, called P-FIND, for example, offers
additional household information like the appraised value of
a home in many states and the number of dependents (both of
which are public information). The main Nexis database digs
up information from news stories -- often from local papers
-- published around the world and in several different
languages. Many of those stories, of course, contain
information of arrests and charges that may later prove
groundless, as well as a volume of personal information about
the people cited in the articles. And the legal Lexis service
contains not only details from civil and criminal court cases
around the nation, but also other potentially sensitive
information, such as tax liens and judgments against
individuals.
Before you get your hackles up over invasion of privacy,
though, consider that all of the above is public information
-- and, of course, much of it is potentially more sensitive
than your previous address.
Patterns exist throughout the universe. Even seemingly chaotic systems reveal
patterns normally hidden by masses of data. Although management may 'gut-feel'
that certain correlations and relationships exist in the data with
which they deal, it is difficult for them to prove these
relationships.
Consumer Pattern Recognition (ConPreg) integrates Predictive
Statistics, Consumer Behaviour and Information Systems. Using well
researched models and algorithms, Pattern Recognition can answer
important business questions such as:
What can any particular customer be expected to buy
and when may they be expected to buy it? =
How can my customers be segmented on the basis of
behaviour (what makes them alike and what makes them
different from each other)? =
What products should I stock where? =
On what basis can I grant credit to which of my
customers? =
Pattern recognition uncovers patterns in how each customer behaves. Factors=
such as - =
time of purchase =
date of purchase =
what was purchased =
amount of purchase =
place of purchase =
go towards creating a pattern for an individual customer - and for
customers just like him (the segment). This pattern is as unique as a
fingerprint. Whether the customer is making a calls on your cellular
network, Using your credit card, purchasing your groceries or buying
your steel, he/she is uniquely identified by this pattern.
Having identified the pattern, we can now predict the customer=92s
behaviour. Behaviour prediction will help the organisation to develop
a high-touch, personal relationship with the customer. For example, a
Consumer Pattern Recognition System can be used as a cornerstone of an
effective database marketing system.
We provide the know-how to either integrate pattern recognition
algorithms into existing information systems or to supply stand-alone
information systems. Pattern Recognition systems can be integrated
within most business systems such as Triton, SAP R/2 & R/3 etc..
Applications & Benefits
A pattern recognition system may be productively used by many functions within an organisation. They include:
Linked to retail outlets, a pattern / behaviour recognition system (PBRS) will be able
to identify consumer needs as and when they occur. Linked to manufacturing facilities,
such a system can react to demand shifts within days.
In banking use, a PBRS will be able to identify =91good=92 and =91bad=92=
accounts and lend
weight to decisions which are normally made on gut feel. Further, a PBRS system will
be able to identify and detect fraud as it is happening as well as accurately predict
account utilisation. Pattern Recognition can also be used to identify customers that are
about to leave (for Banks, it=92s far cheaper keeping a customer than acquiring a new
one). =
Investigators may use Pattern Recognition systems to analyse & simplify complex
economic frauds & scams. =
For Credit Card providers, pattern recognition algorithms can be implemted in
existing information systems to detect and control fraudulent behaviour on the part of
customers and merchants. =
In a Fast Moving Consumer Goods (FMCG) environment, a PBRS will
be give staff information allowing them to bring appropriate items /
specials to the attention of certain customers based on their previous
behaviours / spending patterns.
Used properly, the PBRS will facilitate the =91high touch=92 of a
personal relationship in a fast-moving, rapidly changing mass market
environment. Cellphone Service Providers can use pattern recognition
to detect fraudulent activity on their networks.
Want to learn more? =
Pattern Recognition is a truly multidisciplinary field. If you want
to research further, TRY OUR LINKS or use a search engine (Alta Vista,
HotBot, Lycos, Infoseek etc.) to look for any of the following topics:
Chaos Theory, Complexity Theory, Non-Linear Systems, Complex Systems,
Systems Theory, Consumer Behaviour, Consumer Psychology, General
Systems Theory, Customer Segmentation, Predictive Psychology, Data
Mining, Morphic Field Theory, Pattern Recognition, Forecasting,
Fluctuations, Game Theory, Command Control Systems (normally
associated with military applications) or C4I, Environmental Scanning,
Psychographics, Siftware (data mining software used to extract
information from databases). Or you can e-mail us at kkbb@hot.co.za
-- =
Istvan (Steve) Banhegyi / KKBB Management Systems
P.O.Box 891005, Lyndhurst, 2106 South Africa =
Tel +27 11 882-9661 Fax 882-0859 kkbb@hot.co.za
Previous3NextTop
>~~~Positions:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Wed, 11 Sep 1996 15:36:51 -0400
From: brooksb@verdi.iisd.sra.com
(Bill Brooks x7848)
To: kdd@gte.com
Subject: KDD Positions at SRA International
Content-Length: 2198
SRA International, Inc. is a premier developer of information systems
and provider of a full range of information technology services for
both commercial and federal clients. We are continuing our explosive
growth as a leader in the information industry, as evidenced by our
recent listing as one of the Top 100 Information Technology Services
companies.
SRA is seeking people interested in working in a dynamic, collegial,
advanced technology organization to conduct research, develop
products, and implement systems using Knowledge Discovery in Databases
(KDD). SRA is an industry leader in the development and application
of new KDD technologies. SRA has significant experience, an
established customer base, and a continuing commitment to advancing
the state of the art in a number of areas which are key to processing
large volumes of structured and unstructured data. Areas include
inductive learning, clustering, data conditioning, data visualization,
database optimization, and parallel algorithms.
Our working environment provides a strong synergy between projects and
teams. Many of our projects employ machine learning techniques in the
development of multi-strategy, parallel data mining applications to
solve challenging, real-world problems. If you like the idea of being
immersed in these technologies, have a desire to discuss new
directions and to participate in teams that are charting new
territory, this is the place for you. We have positions open at all
levels. Come and talk to us and find out more.
Requirements: Ph.D. or M.S. in Computer Science (or related field)
with experience in machine learning, data warehousing, or database
design; Strong C/C++ and/or SQL programming skills in Unix and Windows
NT environments desired; Experience in Java, Web-based systems and
products, or on-line services a plus.
Send your resume by mail to SRA, Attn: HB-3, 4300 Fair Lakes
Court, South Building, Suite 500, Fairfax, VA 22033, by fax to
703-227-8268 or 703-803-1509, or by email to careers@sra.com.
SRA is
an equal opportunity employer, m/f/d/v and maintains a drug-free work
force. For further information, please see our home page at http://www.sra.com. Previous4NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Thu, 12 Sep 1996 23:48:42 -0400
From: Todd Eisenbud (toddbud@haven.ios.com)
Subject: Position Available
Data Miner Needed!
I am currently putting together the financing for a start-up KDD/Data
Mining service bureau. I am looking for a motivated, knowledgable
individual who wants to be succesful. Compensation includes salary and
ownership. Please e-mail me at toddbud@haven.ios.com
with a copy of
your resume, salary requirements and a telephone number. Previous5NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Return-Path: (resources@harlequin.co.uk)
Date: Tue, 17 Sep 1996 19:38:15 +0100
To: laws@ai.sri.com,
ai-jobs@cs.cmu.edu,
uai@maillist.CS.ORST.EDU,
ml@ics.uci.edu,
kdd@gte.com
From: andih@harlequin.co.uk
(Andi Hindle)
Subject: New positions at Harlequin Limited.
NEW POSITION
A number of positions have become available at Harlequin Limited.
We need people with real-world experience in object-oriented analysis and
design; statistics and probabilistic inference; scheduling; operations
research; workflow; data mining; machine learning; and web development.
The position will be based in either our
Cambridge, Manchester or Edinburgh, UK
offices or our Menlo Park, US office.
Date: Tue, 17 Sep 1996 15:50:31 -0400 (EDT)
From: Russell Greiner (greiner@scr.siemens.com)
To: laws@ai.sri.com,
ai-jobs@cs.cmu.edu,
kdd@gte.com
Subject: Research/Development Position
The Adaptive Information and Signal Processing Department at
Siemens Corporate Research, Inc. (SCR) in Princeton, New Jersey has
immediate openings for research and development personnel with
experience in one or more of these areas:
machine learning
expert systems (and other areas of AI)
adaptive signal processing
fuzzy logic
user agents
neural networks
intelligent control
To qualify for one of these positions, you should have a PhD with
proven experience in one of the above named areas and a strong
interest in applied research and development aimed at delivering
working prototypes and products to Siemens companies. You must have
very good software development skills including C or C++. Windows API
or OLE is a plus.
Siemens is a world-wide company with sales of more than $60billion and
world-wide employment of almost 400,000. In the US, Siemens has sales
of $6billion and almost 40,000 employees.
Siemens Corporate Research, Inc. employs approximately 140 technical
staff with an emphasis on imaging, multimedia, software engineering
and adaptive systems. SCR's mission is to provide technical expertise
to develop solutions for Siemens operating companies and groups.
Siemens is an Equal Opportunity Employer.
If interested, do **NOT** reply to this message. Send your resume to:
Russell Greiner
Siemens Corporate Research Inc.
755 College Road East
Princeton, NJ 08540
Previous7NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Wed, 18 Sep 1996 10:05:07 -0400 (EDT)
From: Russell Greiner (greiner@scr.siemens.com)
To: ml@ics.uci.edu,
colt@cs.uiuc.edu,
uai@maillist.cs.orst.edu,
connectionists@cs.cmu.edu,
dbworld@cs.wisc.edu,
ai-stats@watstat.uwaterloo.ca,
kdd@gte.com
Subject: Position #2: Research Programmer
The Adaptive Information and Signal Processing Department at
Siemens Corporate Research, Inc. in Princeton, New Jersey has
immediate openings for software development experts with proven
software development skills including
C / C++
MFP
OLE
Familiarity with UNIX and X windows is a plus.
Some experience (course or project work) in one or more of these
technical areas is a significant plus:
machine learning
expert systems (and other areas of AI)
adaptive signal processing
fuzzy logic
user agents
neural networks
intelligent control
To qualify for one of these positions, you should have a master's
degree with proven software development skills and some demonstrated
experience in one of the above technical areas and a strong interest in
collaborating with researchers and development aimed at delivering
working prototypes and products to Siemens companies.
Siemens is a world-wide company with sales of more than $60billion and
world-wide employment of almost 400,000. In the US, Siemens has sales
of $6billion and almost 40,000 employees.
Siemens Corporate Research, Inc. employs approximately 140 technical
staff with an emphasis on imaging, multimedia, software engineering
and adaptive systems. SCR's mission is to provide technical expertise
to develop solutions for Siemens operating companies and groups.
Siemens is an Equal Opportunity Employer.
If interested, do **NOT** reply to this message. Send your resume to:
Russell Greiner
Siemens Corporate Research Inc.
755 College Road East
Princeton, NJ 08540
Date: Fri, 13 Sep 1996 00:11:58 -0500
From: 'Eric King' (eking@ahcsun1.heuristics.com)
Subject: Improve Your Skills in Non-Linear Modeling
Next month, The Gordian Institute will present its popular course, 'Making
Sense of Data: Computer-Aided Pattern Discovery.' If you can benefit by
expanding and improving your toolset of data modelling techniques, this
course is for you! You may never find another that succinctly covers the
essential parts of so many aspects of 'Data Mining' with both theoretical
and practical insights.
To request full details on the October course, 'Making Sense of Data:
Computer-Aided Pattern Discovery,' along with a complete listing of
Gordian Institute's curriculum, simply Email to: gordian@heuristics.com,
or
call 800-405-2114. If you include a fax number or postal address in your
reply, a copy of the catalog will sent via the medium of your choice.
The 3 instructors have more than a decade of experience in applying
adaptive, data-driven techniques to practical problems, and have
substantially refined several of the leading methods covered in this
course. The primary instructor, Dr. John Elder, is an independent
consultant to industry and Adjunct Professor at the University of Virginia,
and has authored four book chapters and numerous articles on adaptive
methods of pattern discovery. He has been a researcher at Rice University
and at an engineering consulting firm, and was Director of Research for an
investment management company.
Dr. Elder is a frequent lecturer on pattern discovery techniques, and is
the technical chair of the Adaptive and Learning Systems Group of the IEEE
Systems, Man, and Cybernetics Society. Also lecturing on, and
demonstrating, algorithms are Paul Hess, co-founder of AbTech Corporation
and president of Hess Consulting; and Dean Abbott, of PAR Government
Systems. Both have successfully authored and applied software employing
polynomial networks, neural networks, and classical statistical methods to
solve a wide variety of industrial problems in statistical estimation and
classification.
The Gordian Institute specializes in the instruction of new software
technologies through first-rate, hands-on intensive training courses in the
fields of:
- Data Mining and Pattern Recognition
- Adaptive Machine Learning
- Intelligent Decision Systems
- Knowledge Engineering
- Hybrid AI Techniques
Again, to request full details on the October 'Making Sense of Data: Computer-
Aided Pattern Discovery' course, along with a complete listing of Gordian
Institute's curriculum, simply Email to: gordian@heuristics.com,
or call 800-
405-2114. If you include a fax number or postal address in your reply, a copy
of the catalog will sent via the either of those media.
______________________________________________
The Gordian Institute
Advanced Software Technology Training
800-405-2114
gordian@heuristics.com
______________________________________________
ECML-97
9th EUROPEAN CONFERENCE ON MACHINE LEARNING
23-26 April 1997, Prague, Czech Republic
-----------------------------------------------------------------------
Up-to-date information on the conference can be found at http://is.vse.cz/ecml97/home.html
_______________________________________________________________________
CHANGES SINCE FIRST CALL FOR PAPERS:
(*) The CALL FOR WORKSHOP PROPOSALS has now been issued.
Look at http://is.vse.cz/ecml97/home.html
or contact
Maarten van Someren (maarten@swi.psy.uva.nl)
for details.
(*) The ECML'97 REVIEWING FORM has been made public
(see http://is.vse.cz/ecml97/home.html.
Have a look to find out according to which criteria your
submission will be evaluated!
(*) The ECML'97 BEST PAPER AWARD is herewith announced. The
decision will be made by the ECML'97 programme committee.
The 9th European Conference on Machine Learning (ECML-97)
will be held in Prague, Czech Republic, during April 23-26, 1997,
with informal workshops on April 26.
The goal of ECML is to be a forum for the discussion of research in
and applications of all forms of machine learning. Although the
emphasis is on scientific advances in machine learning, ECML
also requests papers on applications to practical problems or
to other sciences, provided that general implications of the
application are pointed out.
One of the explicit goals of ECML-97 is to widen the audience and
to strengthen relations between machine learning and other fields
such as statistics, cognitive science, knowledge acquisition,
linguistics, databases, etc.
PROGRAM:
The scientific program (April 23-25) will include invited
talks, presentations of accepted papers, poster and demo
sessions, as well as summary and commenting sessions on current
and upcoming issues in machine learning.
Saturday, April 26, will be devoted to informal workshops, for
which a SEPARATE CALL FOR PROPOSALS has been published
(look at http://is.vse.cz/ecml97/home.html
or contact
Maarten van Someren (maarten@swi.psy.uva.nl)
for details).
RELEVANT RESEARCH AREAS:
Submissions are invited in all areas of Machine Learning,
including, but not limited to:
abduction analogy
applications of machine learning artificial neural networks
case-based learning computational learning theory
evolutionary computation inductive learning
inductive logic programming knowledge base refinement
knowledge discovery in databases knowledge-intensive learning
language learning learning and problem solving
models of human learning multi-agent learning
multistrategy learning reinforcement learning
revision and restructuring robot learning
scientific discovery statistical approaches
PROGRAM CHAIRS:
Maarten van Someren (University of Amsterdam) and
Gerhard Widmer (University of Vienna and Austrian Research
Institute for Artificial Intelligence, Vienna).
LOCAL CHAIR:
Radim Jirousek (University of Economics, Prague).
PROGRAM COMMITTEE:
D. Aha (USA) F. Bergadano (Italy)
I. Bratko (Slovenia) P. Brazdil (Portugal)
K. De Jong (USA) L. De Raedt (Belgium)
S. Dzeroski (Slovenia) W. Emde (Germany)
Y. Kodratoff (France) N. Lavrac (Slovenia)
R. Lopez de Mantaras (Spain) H. Mannila (Finland)
S. Matwin (Canada) K. Morik (Germany)
G. Nakhaeizadeh (Germany) C. Rouveirol (France)
L. Saitta (Italy) J. Schmidhuber (Switzerland)
D. Sleeman (UK) P. Vitanyi (Netherlands)
S. Wrobel (Germany)
SUBMISSION OF PAPERS:
Two kinds of submissions are solicited: full papers describing
substantial completed research or applications, and poster
papers reporting on work in progress. Submissions must be
clearly marked as one of these two kinds.
The programme committee may decide to move accepted
contributions from the full paper to the poster category.
Full papers will be presented at plenary sessions and will
appear in the conference proceedings, poster papers will be
published in a technical report.
The size limit for submissions is 12 pages for full papers,
5 pages for poster papers (excluding title page and bibliography,
but including all tables and figures).
Submissions exceeding this limit will not be reviewed!
The conference proceedings will be published by Springer Verlag
as part of the 'Lecture Notes in AI (LNAI)' series.
Submitted papers should preferably be formatted according to
the LNAI guidelines (LaTeX style files are available at http://is.vse.cz/ecml97/home.html
or by sending an e-mail to
gerhard@ai.univie.ac.at).
The publishers are also considering
to make the proceedings available electronically, before the
conference.
A separate title page must contain the title of the paper,
the names and addresses of all authors, up to three keywords,
and an abstract of max. 200 words. The full address, including
phone, fax and e-mail, must be given for the first author
(or the contact person).
The following items must be submitted by October 21, 1996:
Four (4) hard copies of the paper, an electronic version
(uuencoded, compressed PostScript) of the paper, and an
electronic version of the titlepage only (plain ASCII).
Send submissions, enquiries, etc. to:
Gerhard Widmer (ECML-97)
Austrian Research Institute for Artificial Intelligence,
Schottengasse 3, A-1010 Vienna, Austria
e-mail: gerhard@ai.univie.ac.at
Papers will be evaluated with respect to relevance, technical
soundness, significance, originality, and clarity. Papers
reporting on real-world applications will be evaluated
according to special criteria.
A copy of the review form, which specifies the criteria to
be used in the reviewing process, can be obtained electronically
from http://is.vse.cz/ecml97/home.html.
REGISTRATION AND FURTHER INFORMATION:
For information about paper submission and program, contact
the program chairs. For information about local arrangements,
registration forms, etc. contact the local organizers at
actionm@cuni.cz
or check the ECML-97 WWW page.
IMPORTANT DATES:
Submission deadline: 21 October 1996
Notification of acceptance: 10 January 1997
Camera ready copy: 31 January 1997
Conference: 23-26 April 1997
IMPORTANT ADDRESSES:
Submission of papers to:
Gerhard Widmer (ECML-97)
Austrian Research Institute for Artificial Intelligence,
Schottengasse 3, A-1010 Vienna, Austria
e-mail: gerhard@ai.univie.ac.at
WWW site with conference and registration information,
LaTeX style files, sample review form, etc.: http://is.vse.cz/ecml97/home.html
E-mail address of local conference organization:
actionm@cuni.cz
Previous10NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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