Knowledge Discovery Nuggets 97:10
To KD Mine:
main site for Data Mining and Knowledge Discovery.
Here is how to subscribe to KD Nuggets
Past Issues: 1997 Nuggets,
Knowledge Discovery Nuggets 97:10, e-mailed 97-03-19
J. Brown, Report on DM Summit in San Francisco, Feb 18-21, 1997
B. Pearlmutter, Abbadingo One: DFA Learning Competition
K. Schirmer, smart information services GmbH,
G. John, IBM DATA MINING ANALYST POSITIONS,
B. Perry, HRL Job Opening: Research Intern/Parttime (KDD, DAI, Java)
M. Bramer, Expert Systems 97: Call for Papers
M. Smyth, Hinton-Jordan Learning Methods Tutorial, May 1997,
L. De Raedt, Final call for IJCAI-97 Workshop on
Frontiers of inductive logic programming
S. Dzeroski, ILP-97: CFP Reminder
Knowledge Discovery Nuggets is a free electronic newsletter for the Data Mining
and Knowledge Discovery community, focusing on the latest research and
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~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Ralph Waldo Emerson
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Date: Mon, 17 Mar 1997 21:12:01 -0600
From: 'J.P.Brown' (firstname.lastname@example.org)
Subject: Second Annual Data Mining Summit
The Second Annual Data Mining Summit was held, February 19-21, 1997,
at the San Francisco Regency Hyatt. As I was not at every session, this
is a generalization - no names, no pack drill.
The majority of the delegates were from the United States and Canada.
Nine other countries were represented, from Europe, South America and
Asia. There were presentations all the way from the 'Biggies' to the
'Start-Ups'. From the Past to the Present, there were papers on
specific Data Mining techniques, and much reliance on subjective
approaches. A thought-provoking paper with present-day relevance
covered the Public Perception of Data-Mining. From the Present to the
Future, there were extensions to accepted ideas and some concepts
moving toward a more controversial emphasis on objectivity.
The Basics, and some Specialties, were covered in detail, and
attention was paid to the Dimensions of Decision Support and to
On-Line Analytical Processing, both subjects of great importance.
Some intensely practical, no-nonsense success stories were presented,
and some novel perspectives on iterative 'living' processes.
As well as successful Data Mining examples, Limitations, Challenges
and Possible Pitfalls were pointed out. Solutions were suggested.
Before these demonstrably useful techniques can become the work
horses of the future, a new generation of Tool Support must prove
itself to be effective. This has begun to happen, and the competition
between these new user-friendly applications will be interesting to
Little attention to variations with passage of time, could be noted.
There seems to be a prevalent assumption that 'situations' will not
change. This is 'writing the history of the future' as opposed to the
approach which starts off by 'predicting the past', and then keeps a
constant, trigger-happy lookout for significant change.
The approaches which were considered, varied from simple functions,
to Algorithms, to Genetic Algorithms. Complex hybrid populations
could be separated in several ways. Rules could be used, and
Artificial Neural Nets. Agents could do it, if they were made to be
versatile enough. Visualization was important because we can 'think
with our eyes'. Some of you will know that I am of the 'all of the
>From my own personal point of view the Data Mining Summit was
encouraging. The next move will be to put the pieces together, and to
consciously emphasize our goals. Those who want to know more about
the 'all of the above' school, could try http://www.hal-pc.org/~jpbrown
and then let me know what they think.
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Date: Sun, 9 Mar 97 23:45 MST
From: 'Barak Pearlmutter' (email@example.com)
Subject: Abbadingo One: DFA Learning Competition
Thought database miners might want to whet their teeth on these little datasets. Although neither as big nor as lucrative as the big boys, they are a bit more controlled, and give an opportunity to test an algorithm against all the competition.
Abbadingo One: DFA Learning Competition
Call for Participation
In order to encourage the development of better grammar induction
algorithms, the Abbadingo One competition will award at least $1,024 to
the designer of the system that is most successful at discovering the
structure of random deterministic finite automata, as assessed by a
graded series of nine benchmark problems. The competition ends on
This competition is being sponsored by, among others,
* The Computer Science Department at the University of New Mexico,
which is providing computational support.
* The Kluwer Academic journal 'Machine Learning,' which will give
priority treatment to a paper describing the award winning algorithm.
* The Santa Fe Institute, which will host the award ceremony.
* The 'Journal of Artificial Intelligence Research.'
For details retrieve http://abbadingo.cs.unm.edu/
Good luck, and may the best algorithm win!
Competition Kevin J. Lang (firstname.lastname@example.org)
organizers: Barak A. Pearlmutter (email@example.com)
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[The following is a commercial announcement. GPS]
Date: Tue, 11 Mar 1997 21:30:46 +0100
From: Kai Schirmer (firstname.lastname@example.org)
Subject: smart information services GmbH
We would like to introduce ourselves and are interested in being listed
in your company overview on data mining and knowledge discovery.
Formed in early 1995, smart information services GmbH is located in
Potsdam near Berlin in Germany. The company's activities centers in
application development, service and research using advanced information
technologies in the areas of Intelligent Information Retrieval.
Smart information is currently developing a news categorizing and
filtering system (newscan) using advanced text processing techniques.
Further activities focus on fact extraction from financial news and
automated classification of news from business news wires for signaling,
filtering and routing tasks.
The newscan news filtering system and service offers business
professionals a smartest, easy and cost-effective way of gaining current
awareness in a rapidly changing world. A true knowledge exchange
company, smart information provides electronic information services
which intelligently interconnect content providers and subscribers.
Its interactive, customized services include newscan for corporate
workgroups and enterprises. Newscan is a premium business intelligence
service customized to the specific needs of clients that focuses on the
industry news that's critical to their business. It provides customers
with 'custom-tailored' news based on a profile that describes their
markets, news needs and specialized interests. Using advanced filtering
techiques, newscan selects highly relevant news by scanning some 3,000
to 4,000 German and English news daily and delivers only those relevant
to each customer in time for each business day.
Smart information is partner in the Esprit project ECRAN. ECRAN will
develop a new generation of Information Extraction (IE) applications, to
be included in telematic services having a large textual content. ECRAN
will analyse free texts (initially, financial information from
specialised newswire services, and market information on the internet)
extracting information content. The information can be compared against
a model of user requirements so that the system can precisely identify
text of interest to a customer.
By using the results of the ECRAN project specific financial, economic
and political information from standardised news will be extracted and
stored in a database format. The information extraction is based on
lexicon tuning technologies and sophisticated template handling. Once
stored in a database format the extracted facts can be analysed in
combination with time series.
Currently smart information is preparing a European research project on
information mining in heterogeneous environments. The main ideas are
described in the following.
In the past few years, the abundance of continuous data sources, the
connectivity allowed by local and worldwide public and private networks,
and the continuous decrease of the bandwidth/price ratio, have been
subject to a steady growth at explosive rates, and this trend has shown
no sign of decline ever since. Thus, staggering amounts of new
information are continuously made available to private users, business
firms and professional operators. Extracting the information relevant
for a given business or position from an overwhelming flood of data, and
being able to use it for tactical and strategical planning, as well as
decision support on the fly, is vital for business survival and
leadership, but it is getting less and less amenable of human handling.
On the other hand, an ever increasing part of current information fluxes
passes through computer networks, which makes them amenable of automatic
filtering, processing and interpretation. Both situations concur to
demonstrate both the need and the feasibility of systems that filter and
integrate information from different data sources, sometimes being
static and well structured (legacy Data bases), sometimes dynamic and
with a variable degree of standardization, from rigidly defined records,
to multimedia documents, to free text, speech, images.
Please link to our web-site 'www.newscan-online.de'.
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Date: Tue, 11 Mar 1997 20:43:00 -0800 (PST)
From: George John (gjohn@robotics.Stanford.EDU)
Subject: IBM DATA MINING ANALYST POSITIONS (please post/redistribute)
IBM DATA MINING ANALYST POSITIONS (please post/redistribute)
Help! We're drowning in work! IBM needs 10 more analysts for its
highly successful data mining group. Join our team of high-caliber
PhD's in an exciting multi-faceted career in data mining:
* Analyze data for customers using IBM's industry-leading data mining
* Interact directly with senior management at Fortune 500 companies
* Teach data mining classes to our customers and develop course materials
* Travel, see the world! (One member of our team just got back
from Paris, another is heading to Australia for two weeks... these
are not vacations, it's their job!)
* Interact with researchers and product developers, discuss ideas for
new data mining algorithms, new visualizations, and new features
for our products
* Assist sales reps in customer visits, be the 'technical person' to
answer hard questions
* Work with the marketing group to help develop brochures, etc.
* Attend trade shows and conferences, learn more about the industry
and talk to customers
* Use SQL/AWK/PERL/SAS to process data (ooh, the excitement!)
The ideal candidate
* has an excellent understanding of the data analysis process and has
participated in several projects
* is strongly technically proficient in at least some areas of data
mining (background in statistics, machine learning, neural nets, or
pattern recognition, or related), with a desire to learn more
* has excellent communication and presentation skills
* is a self-starter, good at quickly becoming a productive member of
* is a fast learner, can quickly become an expert in a new industry
and work with IBM consultants to productively apply data mining
* has some unix skills, knows enough AWK and PERL to be self-sufficient
in processing data
* has a good sense of humor, fun to work with, enjoys taking co-workers
out to dinner, insists on paying every time, etc...
Positions are available for both senior applicants (professors, PhD's,
MBA's, or 4+ years relevant business experience) and more junior members
(MS, BS, less job experience). Salaries are competitive, and based on
experience. The jobs are focused on business, but some amount of time
spent on research may be negotiated. IBM's data mining group is growing
quickly, and offers excellent career opportunities.
For more information on data mining at IBM, see the webpage for IBM
Global Business Intelligence Solutions (our parent organization) at
Send resume to George H. John, email@example.com.
ASCII (plain text) via email is *strongly* preferred.
Please put 'DMJOBS-97:' then your name in the subject.
Hardcopy may be sent to
George H. John
IBM Alamden Research Center
650 Harry Rd / D2
San Jose, CA 95120-6099
FAX: 408-927-2100 (put 'Attn: George John' on cover sheet)
IBM is an equal opportunity employer.
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Date: Wed, 12 Mar 1997 15:25:34 -0800
Subject: HRL Job Opening: Research Intern/Parttime (KDD, DAI, Java) http://www.wins.hrl.com
Subject: HRL Job Opening: Research Intern/Parttime (KDD, DAI, Java)
We are currently looking to fill an intern, or part-time, PhD candidate at Hughes Research Laboratories (HRL). The position will be a summer intern capable of extending into a part-time position during the school year. HRL is located in Malibu, CA and represents the central research lab for Hughes Electronics Corporation.
Our group is investigating the use of agent, data mining, and database technologies to support information management, discovery, and analysis in large-scale dynamic Internet environments.
Our two primary research areas involve:
* Information exploitation techniques to effectively identify and disseminate semantically relevant information to large user populations, especially with the use of satellite broadcast channels.
* Data mining techniques to extract, represent, and manipulate semantic cues from large-scale and distributed information sources.
The candidate should have a background in DAI, agent architectures, machine learning, and data mining. Experience with KQML, KIF, and/or Java a definite plus. This position entails research and prototype development.
* PhD candidate in Computer Science (or related field)
* Good OO programming skills (implementation of prototypes will
* Unix programming background.
* Good oral and written communication skills.
* Machine Learning or Data Mining background
* Java programming experience (or C/C++, at least).
* Multidatabase systems.
* Distributed object systems (CORBA, RMI, etc.)
Please email your resume to Son Dao at firstname.lastname@example.org,
or mail to:
Hughes Research Laboratories
3011 Malibu Canyon Road
Malibu, CA 90265
HRL is an equal opportunity employer.
Hughes Research Laboratories email@example.com
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From: 'Max Bramer' (firstname.lastname@example.org)
Date: Sun, 9 Mar 1997 17:05:52 +0000
Subject: Expert Systems 97: Call for Papers
BRITISH COMPUTER SOCIETY
SPECIALIST GROUP ON EXPERT SYSTEMS
ANNUAL CONFERENCE - EXPERT SYSTEMS '97 (ES97)
CALL FOR PAPERS
The 17th annual Conference of the British Computer Society Specialist Group
on Expert Systems, ES97, is being held at St. John's College, Cambridge
between 15th and 17th December 1997. The objective of the ES series of
conferences is to bring together researchers and application developers
from business, industrial and academic communities to discuss issues and
solutions to problems based on techniques derived from Artificial
The Conference continues to build on the success of previous years, with a
two-track event containing fully refereed technical and applications
For the Technical Stream, contributions are invited in the form of papers
of up to 5,000 words on knowledge-based systems and related areas of
Artificial Intelligence. Papers representing original work on theoretical
and applied AI relating to: constraint satisfaction; intelligent agents;
knowledge engineering methods; machine learning; model-based reasoning;
verification and validation of KBS; natural language understanding;
case-based reasoning, knowledge discovery in databases and other related
areas are welcome.
For the Applications Stream, contributions are invited in the form of
papers of up to 5,000 words presenting case studies of knowledge based
systems that address real-world problems such as: diagnosis, monitoring,
scheduling and selection. Most importantly, the papers should highlight the
critical elements of success and the lessons learned.
Papers submitted to both streams will be refereed and those accepted will
again be published in book form in the 'Research and Development in Expert
Systems' and 'Applications and Innovations in Expert Systems' series (for
the technical and application streams respectively).
To assist us with our planning of the conference, anyone intending to
submit a paper should provide a short abstract, with title, at the earliest
opportunity to the Conference Secretariat.
Authors should indicate the stream to which their papers are being
submitted. Please include your full name and postal address in any email
Formatting instructions for papers will be sent as soon as the title and
abstract are received.
Four copies of papers should be submitted to arrive no later than Friday
20th June 1997. Submissions should be sent in paper form by post to the
Please note that presenters of submitted papers will be asked to cover
their costs of attending the conference by paying at the SGES members'
TUTORIALS & WORKSHOPS
The Conference Committee invites proposals for tutorials or workshops to be
presented on Monday 15 December. Proposals for full and half day tutorials,
from an individual or group of presenters should be directed in the first
instance to the Conference Secretariat.
A table top exhibition will run alongside the Conference. There will be a
limited number of spaces available and potential exhibitors are encouraged
to book early, as these will be on a first-come, first-served basis.
The Conference Committee is keen to make contact with any organisations who
may wish to sponsor the Conference, in whole or in part. Sponsorship of an
international conference such as ES97 will ensure the highest visibility
for the benefactor, both through the appearance of the company logo on all
promotional literature and in references to the Conference in all media
exposure prior to and after the event.
Conference Chair: Prof Max Bramer, University of Portsmouth, Southsea, PO4
Deputy Conference Chair: Dr Ian Watson, University of Salford, Salford, M5
Technical Programme Chair: Dr John Hunt, University of Wales, Dept of
Computer Science, Aberystwyth, Dyfed SY23 3DB email@example.com
Applications Programme Chair: Mrs Ann Macintosh, Artificial Intelligence
Applications Institute, Edinburgh, EH1 1HN firstname.lastname@example.org
Ms. Kit Stones, The Conference Team
17 Spring Road
Kempston, Bedford MK42 8LS
Tel/Fax +44 (0)1234-302490
Title/Abstract notification: now
Full paper submission: 20 June 1997
Notification of acceptance: 8 August 1997
Camera ready papers due: 19 September 1997
WORLD WIDE WEB ADDRESS FOR CONFERENCE INFORMATION:
Professor Max Bramer
Department of Information Science
University of Portsmouth
Milton, Southsea PO4 8JF, England
Tel: +44-(0)1705-844444 Fax: +44-(0)1705-844006
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From: Marney Smyth (email@example.com)
Subject: Hinton-Jordan Learning Methods Tutorial, May 1997
Date: Mon, 10 Mar 1997 06:09:19 -0500 (EST)
*** Learning Methods for Prediction, Classification, ***
*** Novelty Detection and Time Series Analysis ***
*** Washington, D.C., May 2 -- 3, 1997 ***
*** Geoffrey Hinton, University of Toronto ***
*** Michael Jordan, Massachusetts Inst. of Tech. ***
A two-day intensive Tutorial on Advanced Learning Methods will be held
May 2 -- 3rd, 1997, at the Hyatt Regency on Capitol Hill, Washington
D.C. Space is available for up to 50 participants for the course.
The course will provide an in-depth discussion of the large collection
of new tools that have become available in recent years for developing
autonomous learning systems and for aiding in the analysis of complex
multivariate data. These tools include neural networks, hidden Markov
models, belief networks, decision trees, memory-based methods, as well
as increasingly sophisticated combinations of these architectures.
Applications include prediction, classification, fault detection, time
series analysis, diagnosis, optimization, system identification and
control, exploratory data analysis and many other problems in
statistics, machine learning and data mining.
The course will be devoted equally to the conceptual foundations of
recent developments in machine learning and to the deployment of these
tools in applied settings. Case studies will be described to show how
learning systems can be developed in real-world settings. Architectures
and algorithms will be presented in some detail, but with a minimum of
mathematical formalism and with a focus on intuitive understanding.
Emphasis will be placed on using machine methods as tools that can be
combined to solve the problem at hand.
WHO SHOULD ATTEND THIS COURSE?
The course is intended for engineers, data analysts, scientists,
managers and others who would like to understand the basic principles
underlying learning systems. The focus will be on neural network models
and related graphical models such as mixture models, hidden Markov
models, Kalman filters and belief networks. No previous exposure to
machine learning algorithms is necessary although a degree in
engineering or science (or equivalent experience) is desirable. Those
attending can expect to gain an understanding of the current
state-of-the-art in machine learning and be in a position to make
informed decisions about whether this technology is relevant to specific
problems in their area of interest.
Overview of learning systems; LMS, perceptrons and support vectors;
generalized linear models; multilayer networks; recurrent networks;
weight decay, regularization and committees; optimization methods;
active learning; applications to prediction, classification and control
Graphical models: Markov random fields and Bayesian belief networks;
junction trees and probabilistic message passing; calculating most
probable configurations; Boltzmann machines; influence diagrams;
structure learning algorithms; applications to diagnosis, density
estimation, novelty detection and sensitivity analysis
Clustering; mixture models; mixtures of experts models; the EM
algorithm; decision trees; hidden Markov models; variations on hidden
Markov models; applications to prediction, classification and time
Subspace methods; mixtures of principal component modules; factor
analysis and its relation to PCA; Kalman filtering; switching mixtures
of Kalman filters; tree-structured Kalman filters; applications to
novelty detection and system identification
Approximate methods: sampling methods, variational methods; graphical
models with sigmoid units and noisy-OR units; factorial HMMs; the
Helmholtz machine; computationally efficient upper and lower bounds for
Standard Registration: $700
Student Registration: $400
Cancellation Policy: Cancellation before Friday April 25th, 1997, incurs
a penalty of $150.00. Cancellation after Friday April 25th, 1997, incurs
a penalty of one-half of Registration Fee.
Registration Fee includes Course Materials, breakfast, coffee breaks, and lunch.
On-site Registration is possible. Payment of on-site registration must
be in US Dollar amounts, by Money Order or Check (preferably drawn on a
US Bank account).
Those interested in participating should return the completed
Registration Form and Fee as soon as possible, as the total number of
places is limited by the size of the venue.
[edited for space]
A registration form and hotel information
are available from the course's WWW page at
Phone: 617 258-8928
Fax: 617 258-6779
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Date: Fri, 14 Mar 1997 16:47:02 +0100 (MET)
From: Luc De Raedt (Luc.DeRaedt@cs.kuleuven.ac.be)
Subject: Final CFP Frontiers of ILP Workshop at IJCAI
FINAL CALL FOR PARTICIPATION and PAPERS
IJCAI-97 Workshop on
FRONTIERS OF INDUCTIVE LOGIC PROGRAMMING
Monday 25 August 1997
The IJCAI-97 one day workshop on 'Frontiers of ILP' in Nagoya, Japan,
will take place on August 25, immediately prior to
the start of the main IJCAI conference.
Inductive logic programming (ILP) is a recent subfield of
artificial intelligence that studies the induction of first order formulae
from examples. The purpose of this workshop is twofold:
on the one hand, we wish to widen the scope of ILP
by investigating its relations to neighboring fields,
and on the other hand, we wish to make ILP more accessible
for researchers from neighboring fields.
The workshop therefore solicits papers
that lie at the frontiers of ILP with neighboring fields.
A non-exclusive list of interesting topics for the workshop includes :
* ILP and Software Engineering:
what has ILP to offer to Software Engineering ?,
and in what way can Software Engineering help to design ILP systems
and applications ?
* ILP for Knowledge Discovery in Databases : ILP aims
at learning complex rules involving multiple relations from small
databases, whereas KDD typically induces simple rules about a
single relation from a large database. Furthermore, ILP allows to
exploit background knowledge in a variety of ways. Can KDD and ILP be
succesfully combined ?
* ILP and Computational or Algorithmic Learning Theory :
though many results have been obtained concerning the learnability
of inductive logic programming, most of the results are negative
and most of the positive results are reducible to propositional learning
methods. Is there a mismatch of COLT with ILP ? and if so,
what can be done about it ?
* ILP versus propositional learning methods :
Since the very start of ILP, researchers and practioners of
machine learning have wondered about the relation between
ILP and propositional learning methods. Theoretical and experimental
questions that arise include:
when to use ILP and when to use propositional learning methods ?
under what circumstances can ILP be reduced to propositional learning ?
what is the price to pay for using first order logic in
terms of efficiency ?
* ILP and Knowledge Representation : ILP has traditionally employed
computational logic to represent hypotheses and observations.
Alternative well-founded knowledge representation formalisms have received
little attention (with the exception of CLASSIC).
What can ILP learn from Knowledge Representation ?
and in what well-founded Knowledge Representation formalisms
is induction feasible ?
* ILP in multistrategy learning : Multistrategy learning
combines multiple learning strategies. What role can ILP
play for multistrategy learning ?
* ILP and Probabilistic reasoning: in contrast to
propositional learning methods, ILP has not used
probabilistic representations. How can ILP incorporate
such representations ? and how can it interact with
methods such as Bayes nets or Hidden Markov Models ?
* ILP for Intelligent Information Retrieval:
The rapid development of
the World Wide Web has spawned significant interest in intelligent
information retrieval. In particular, the need for algorithms for
reliably classifying textual documents into given categories (like
interesting/uninteresting) be useful for a wide variety of tasks.
Currently, most learning algorithms are not able to make use of
structural information like word order, succesive words, structure of
the text, etc. Can ILP algorithms offer advantages over conventional
information retrieval or machine learning algorithms for this sort of
* Applications of ILP in subfields of AI : ILP has been applied
to other subfields of AI, including natural language processing,
intelligent agents and planning.
Further applications of ILP within AI are solicited.
Both position papers about the relation of ILP to other fields, as well
as research papers that make specific techical contributions
are solicited. However, to stimulate discussion, it is expected
that each technical paper also clarifies the position
of ILP with regard to the neighboring field(s) it addresses.
Except for the presentation of position and technical papers,
the workshop will also feature a panel discussion
on the frontiers of ILP and possibly an invited talk.
Luc De Raedt (chair and primary contact)
Francesco Bergadano (Italy)
Luc De Raedt (co-chair, Belgium)
Saso Dzeroski (Slovenia)
Johannes Furnkranz (Austria)
Koichi Furukawa (Japan)
David Page (U.K.)
Fumio Mizoguchi (Japan)
Ray Mooney (U.S.A.)
Stephen Muggleton (co-chair, U.K.)
CALL FOR PARTICIPATION
Participation is open to all members of the AI Community.
However, to encourage interaction and a broad exchange of ideas
the number of participants will be strictly limited
(preferably under 30 and certainly under 40).
Participants will be selected on the basis of submissions.
Three types of submissions will be considered :
1) technical contributions (ideally, a 3 to 5 page extended abstract,
in the IJCAI Proceedings Format, 3000-4000 words),
2) position papers (ideally, a 1 to 3 page abstract
in the IJCAI Proceedings Format, 1000 - 3000 words)
3) a statement of interest (ideally, a one page motivation of why you
would like to participate, 300- 500 words)
Only submissions of type 1) and 2) will be considered
for presentation at the workshop and inclusion in the workshop notes.
Submissions should be received no later than April 1, 1997,
and must include first author's complete contact information,
including address, email, phone, and fax number. Though 1 April
is the hard deadline, the authors are encouraged to submit
their material by 24 March, in order to facilitate the reviewing process.
Double submissions with the ILP-97 Workshop (which is to take
place in Prague, September 1997) are allowed.
Submit papers by email (postscript) and surface mail (2 copies) to
Luc De Raedt
Dept. of Computer Science
Katholieke Universiteit Leuven
Email : Luc.DeRaedt@cs.kuleuven.ac.be
- Paper submission : 1 April
- Notification to Authors : 21 April
- Camera ready copy : the submissions themselve
will serve as camera ready copy
(submissions in the IJCAI Proceedings Style are strongly preferred,
The accepted submissions will be included in the workshop notes
to be distributed at the workshop.
Post-conference publication of a selection of the workshop papers
will be considered and discussed at the workshop.
To cover costs, a fee of $US 50 will be charged,
in addition to the normal IJCAI-97 conference registration fee.
Attendees of IJCAI workshops will be required to register
for the main IJCAI conference.
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Subject: ILP-97: CFP Reminder
Date: Mon, 17 Mar 1997 15:49:23 +0100
From: Saso Dzeroski (Saso.Dzeroski@ijs.si)
The Seventh International Workshop on
Inductive Logic Programming
17-19 September 1997, Prague, Czech Republic
The deadline for paper submissions is 31 March 1997.
Invited talks will include:
'Data Mining: Algorithms and Limitations' by Usama Fayyad,
'Complexity of Logic Programming' by Georg Gottlob, and
'ILP and CLP' by Jean-Francois Puget.
For more information see
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