Knowledge Discovery Nuggets Index


To KDNuggets Directory   |   Here is how to subscribe to KD Nuggets   |   This Year   |   Past Issues

Knowledge Discovery Nuggets(tm) 98:3, e-mailed 98-02-07


News:
  • (text) GPS, Gartner Group: data mining is one of top 10 technologies for 1998
  • (text) GPS, KDD-98 Awards for Best Research and Application Papers

    Publications:
  • (text) Saso Dzeroski PhD, REMINDER: DMKD Special Issue on ILP&KDD
    Deadline 15 FEB, 1998,
    http://www-ai.ijs.si/SasoDzeroski/dami.cfp
  • (text) H Michael Chung, JMIS Call for Papers on Data Mining and
    Knowledge Discovery,
    http://www.csulb.edu/~cit/cit/call.html#call
  • (text) Maria Zemankova, NSF Knowledge & Distributed Intelligence:
    letters due 4/1/98,
    http://www.nsf.gov/cgi-bin/getpub?nsf9855

    Siftware:
  • (text) David Jensen, Evaluation of Intelligent Systems -- New Website

    Positions:
  • (text) Shannon Pemberton, Data Mining Employent Opportunities at InsWeb, CA
  • (text) Walter Daelemans, LCG TMR Network: Research Opportunities, Europe
  • (text) Chandrika Kamath, Employment opportunity at Lawrence Livermore Lab

    Meetings:
  • (text) Foster Provost, REMINDER: KDD-98 paper submissions due in March
    http://www-aig.jpl.nasa.gov/public/kdd98/
  • (text) Gen Topping, Data Mining Summit, March 1-4, 1998, Beverly Hills, CA
    http://www.dbsummit.com/
  • (text) Melanie Hilario, ECML'98 WS - Upgrading Learning to the Meta-Level
    http://www.cs.bris.ac.uk/~cgc/ecml98-ws.html
  • (text) G. Nakhaeizadeh, ECML'98 WS - Application of Machine Learning and
    Data Mining in Finance,
    http://www.tu-chemnitz.de/informatik/ecml98
  • (text) Vincent Corruble, ECAI-98 WORKSHOP ON MACHINE DISCOVERY,
    August 24, 1998, Brighton, U.K
    http://www.csd.abdn.ac.uk/~vcorrubl/disco98/cfp.html
  • (text) Riccardo Bellazzi, ECAI-98 Workshop on Inteligent Data Analysis in
    Medicine and Pharmacology, August 24, 1998, Brighton, UK
    http://aim.unipv.it/~ric/idamap98
    --
    Data Mining and Knowledge Discovery community, focusing on the
    latest research and applications.

    Submissions are most welcome and should be emailed, with a
    DESCRIPTIVE subject line (and a URL) to gps.
    Please keep CFP and meetings announcements short and provide
    a URL for details.

    To subscribe, see http://www.kdnuggets.com/subscribe.html

    KD Nuggets frequency is 2-3 times a month.
    Back issues of KD Nuggets, a catalog of data mining tools
    ('Siftware'), pointers to Data Mining Companies, Relevant Websites,
    Meetings, and more is available at Knowledge Discovery Mine site
    at http://www.kdnuggets.com/

    -- Gregory Piatetsky-Shapiro (editor)
    gps

    ********************* Official disclaimer ***************************
    All opinions expressed herein are those of the contributors and not
    necessarily of their respective employers (or of KD Nuggets)
    *********************************************************************

    ~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    To give your cow or sheep a large, spacious meadow is the best way to control him
    Source unknown
    Previous  1 Next   Top
    Date: 30 Jan 1998 10:36:16 -0500
    From: GPS gps

    According to market research firm Gartner Group Inc,
    the top 10 technologies to
    watch in 1998 are, in order: biometrics, video conferencing, data mining,
    document imaging, electronic cash, network computers, personal digital
    assistants, push technology, smart cards and speech recognition.

    Source: http://www.newspage.com/

    Previous  2 Next   Top
    Date: Mon, 19 Jan 1998 14:16:16 -0500
    From: GPS gps
    Subject: KDD-98 Awards for Best Research and Application Papers

    I am very pleased to announce that Knowledge Stream Partners will again
    sponsor the KDD-98 best paper awards for research and application.

    The best research paper will be selected based on novelty and technical quality of
    its ideas and the significance of its theoretical contribution. An
    application paper will be selected based on practical
    significance of the application and the innovative application of the technology.

    The papers will be selected by a subset of KDD-98 Program Committee and
    the awards will be presented at KDD-98.

    The amount of each award has doubled from last year and will be $1,000.

    KDD-98 web site is at http://www-aig.jpl.nasa.gov/public/kdd98/

    Previous  3 Next   Top
    From: Saso Dzeroski PhD Saso.Dzeroski@ijs.si
    Subject: REMINDER: DAMI Special Issue on ILP&KDD Deadline 15 FEB
    Date: Thu, 22 Jan 1998 14:20:40 MET
    -----------------------------------------------------------------------------
    REMINDER: Data Mining and Knowledge Discovery journal, Special issue on
    Inductive Logic Programming and Knowledge Discovery in Databases

    Guest editors: Saso Dzeroski and Nada Lavrac, J. Stefan Institute, Ljubljana

    Submission deadline: 15 FEB 1998

    For submission instructions see the full CFP at
    http://www.research.microsoft.com/datamine/dmkd-ilp/
    http://www-ai.ijs.si/SasoDzeroski/dami.cfp
    -----------------------------------------------------------------------------
    Knowledge Discovery in Databases (KDD) is concerned with identifying
    interesting patterns in data and describing them in a concise and
    meaningful manner. In KDD, machine learning tools are often used for
    data mining and are thus present in many KDD systems and applications.
    However, most of these tools use a propositional representation of
    both the data analysed and the knowledge being discovered, mining in
    effect a single relational table in a given database.

    Inductive Logic Programming (ILP) can be viewed as machine learning
    in a first-order language, where both the data analysed and the patterns
    considered can involve several relations in a relational database. Using
    ILP tools for data mining offers several advantages, including the
    expressiveness of first-order logic as a representation language,
    the ability to use structured data as well as various forms of
    background knowledge and the ability to use language bias provided
    by the user to define the search space of patterns considered.

    The special issue on Inductive Logic Programming and Knowledge
    Discovery in Databases of the journal Data Mining and Knowledge
    Discovery welcomes papers that focus on algorithms and applications
    that involve the discovery of knowledge expressed in a relational or
    first-order formalism.

    <>
    Previous  4 Next   Top
    Date: Mon, 02 Feb 1998 13:59:38 -0800
    From: H Michael Chung hmchung@csulb.edu
    Subject: JMIS Call for Papers on Data Mining and Knowledge Discovery

    CALL FOR PAPERS

    Journal of Management Information Systems

    Special Section on

    'Data Mining and Knowledge Discovery'

    The special section focuses on the management and business applications
    of data mining,

    inductive learning, knowledge acquisition, knowledge discovery, and
    inductive decision making.

    Deadlines are  March 1: (Optional Abstract due) and June 1: 
    (Full Paper due).


  • For details, see

    • http://www.csulb.edu/~cit/cit/call.html#call/A.

    Please contact H.M .Chung (Special Section Co-Editor) for further questions.

    Thank you.

    ***********************************************

    H. Michael Chung, Ph.D.

    Associate Professor

    Department of Information Systems

    College of Business Administration

    California State University, Long Beach (CSULB)

    Long Beach, CA 90840-8506

    USA

     

    TEL    (562) 985-7691

    FAX    (562) 985-5543

    EMAIL  hmchung@csulb.edu

    ***********************************************

     

    Previous  5 Next   Top
    Date: Tue, 3 Feb 1998 13:19:37 -0500
    From: Maria Zemankova mzemanko@nsf.gov
    Subject: NSF Knowledge & Distributed Intelligence: letters due 4/1/98

    The following document (nsf9855) is now available from
    the NSF Online Document System

    Title: KDI: Knowledge and Distributed Intelligence (NSF 98-55)
    Type: Program Announcements & Information
    Subtype: Computer/Information Sciences, Crosscutting Programs,
    Education, Social/Behavioral Sciences

    It may be found at:

    http://www.nsf.gov/cgi-bin/getpub?nsf9855

    Deadlines:
    =========
    April 1, 1998: Letter of intent due.
    May 8, 1998: Deadline for full proposals.

    Next proposal deadline will be February 1, 1999
    ______________________________________________________________

    For additional information on KDI, see http://www.cise.nsf.gov/iis/kdi.html.

    Previous  6 Next   Top
    Subject: Evaluation of Intelligent Systems -- New Website
    Date: Mon, 2 Feb 98 16:00:54 -0500
    From: David Jensen jensen@cs.umass.edu

    Evaluation of Intelligent Systems
    http://eksl-www.cs.umass.edu/eis/

    Evaluation of Intelligent Systems (EIS) is an online resource
    that provides 'one-stop shopping' for researchers, managers,
    system-builders, and users who wish to study the empirical
    behavior of information systems.

    EIS covers:

    - Exploratory data analysis
    - Hypothesis testing
    - Modeling
    - Statistical terminology

    EIS also serves a community-building function through:

    - Announcements of meetings, publications, funding
    opportunities, and research projects.
    - An advice column that provides timely responses to specific
    methodological questions from users.
    - Links to external resources such as data repositories,
    bibliographies, and publications.

    Users can submit questions, comments, announcements, and
    suggested links via online forms.

    EIS was developed by The Experimental Knowledge Systems
    Laboratory at the University of Massachusetts Computer Science
    Department and the Colorado State University Computer Science
    Department. The project was managed by Sterling Software and
    funded by the Air Force Research Laboratory's Knowledge
    Engineering Branch. Substantial portions of EIS are adapted from
    Paul Cohen's 1995 textbook 'Empirical Methods for Artificial
    Intelligence,' courtesy of MIT Press.

    The current and future content of EIS is overseen by its
    editors and editorial board:

    Paul Cohen (Editor-in-Chief), University of Massachusetts
    David Jensen (Managing Editor), University of Massachusetts
    Paul Losiewicz (Managing Editor), Sterling Software

    Craig Anken, Air Force Research Laboratory
    Michael Berthold, University of California, Berkeley
    Thomas Dietterich, Oregon State University
    Pedro Domingos, Instituto Superior Tecnico, Lisbon
    David Hand, The Open University
    Lynette Hirschman, MITRE Corp.
    Louis Hoebel, GE Corporate Research and Development
    Adele Howe, Colorado State University
    Xiaohui Liu, Birkbeck College, University of London
    Mark Musen, Stanford University
    Peter Norvig, Junglee Corp.
    Adam Pease, Teknowledge Corp.
    Bruce Porter, University of Texas at Austin
    Marco Ramoni, The Open University
    Robert Schrag, Information Extraction & Transport, Inc.
    Paola Sebastiani, City University, London

    Previous  7 Next   Top
    From: Shannon Pemberton spemberton@insweb.com
    To: KD Nuggets Editor gps
    Subject: Data Mining Employent Opportunities
    Date: Wed, 21 Jan 1998 09:29:41 -0800

    Title: VP, Customer Data Mining and Market Research

    Established in March 1995, InsWeb Corporation, San Mateo, CA
  • (www.insweb.com)
    • is an exciting, fast growing technology company.
    InsWeb has become the most diverse and inclusive insurance site on the
    World Wide Web. InsWeb has created a 'one stop shopping' forum for
    insurance consumers to gather price quotations and product information
    abut various types of insurance products from multiple insurance
    providers using the Internet. The successful candidate will be an
    important member of a close-knit team of technology professionals.

    Primary Responsibilities: Mine data from a national customer database to
    develop customer segmentation model. Apply findings across a spectrum
    of account acquisition and cross -selling activities. Develop customer
    profitability models, usage patterns, and relationship profiles. Manage
    the customer database to generate customer e-mailing lists. Interface
    with technical staff on file management issues and recommend
    improvements and enhancements. Mine data to gain insights about
    customer behavior and recommend product enhancements and modifications
    to meet these opportunities. Manage all marketing program tracking and
    reporting activities. Recommend and execute qualitative and
    quantitative market research programs using internal and external
    resources. Interpret findings and identify opportunities and risks.
    Design, implement and interpret customer satisfaction surveys.

    The ideal candidate must possess: 8-12 years project management
    experience in market research and database management; at least five
    years in a managerial capacity. Ability to create data inferences and
    translate findings into meaningful marketing insights and strategies.
    Proven track record of results and success ability to build an
    organization; identify develop and implement policies and procedures
    highly organized thinker with strong project management skills.
    Experience in financial services sector (insurance, brokerage, banking).
    Strong analytical, communication, leadership and people management
    skills. MBA or other advanced degree preferred.

    Contact: Shannon Pemberton
    Submit your resume:
    By Mail: 3000 Executive Pkwy
    Suite 530
    San Ramon, CA 94583
    * By Fax: (510) 830-9081
    * By E-mail: resumes@insweb.com
    * Visit our site:
  • www.insweb.com


    • Previous  8 Next   Top
    From: Walter.Daelemans@kub.nl
    Date: Thu, 22 Jan 1998 17:44:47 +0100 (MET)
    Subject: Jobs: LCG TMR Network: Research Opportunities

    *************************************
    * LCG TMR Network *
    * Learning Computational Grammars *
    * *
    *************************************

    ***************************************
    * POSTDOCTORAL RESEARCH OPPORTUNITIES *
    ***************************************

    with potential roles for graduate-student level participation
    (see below).

    LCG (Learning Computational Grammars) is a research network
    shortlisted for funding by the EC Training and Mobility of Researchers
    programme (TMR). LCG's contract is currently being negotiated. The
    network is expected to run from 1st March 1998 for three years.

    LCG will research the application of a variety of machine learning
    techniques to natural language syntax, including techniques from
    neural networks, statistical learning, and symbolic learning. Their
    will be a focus comparison of results based on attempts to learn noun
    phrase structure in English. Some work on other languages is possible.

    The LCG partners are

    University of Groningen, Netherlands (coordinator)
    University of Tuebingen, Germany
    SRI, The United Kingdom
    University of Antwerp, Belgium
    University College Dublin Ireland
    ISSCO, Switzerland
    Rank Xerox, France

    For more details of LCG's programme, see

    http://www.let.rug.nl/~nerbonne/tmr/lcg.html

    If you are interested, send your CV (including publication list) and
    the names and addresses of two referees to the address below. Indicate
    what LCG tasks (see the web site) and what LCG labs interest you, and
    when you expect to be available. Please indicate in a 2-3 pp. sketch
    of your interest in LCG how it is related to work you have done and
    what special expertise you bring to the problem.

    John Nerbonne, Alfa Informatica nerbonne@let.rug.nl
    University of Groningen Tel. (31) +50 363 5815
    P.O. Box 716 Fax 363 6855
    Oude Kijk in 't Jatstraat 26
    NL-9700 AS GRONINGEN http://www.let.rug.nl/~nerbonne
    The Netherlands

    Previous  9 Next   Top
    Date: Thu, 22 Jan 1998 17:59:16 -0800 (PST)
    From: Chandrika Kamath kamath@antiope.llnl.gov
    Subject: Employment opportunity at Lawrence Livermore Lab

    Computer Science Post Doctoral Researcher

    The Center for Applied Scientific Computing is seeking a computer
    scientist interested in doing research and development in the area of
    data mining, in particular pattern recognition and probabilistic error
    modeling. Will be a member of a team responsible for developing a
    capability for the prediction of complex phenomena in large scale
    scientific settings. Will also be expected to pursue independent (but
    complementary) research interests. Duties include designing and
    implementing algorithms, developing research plans, and conducting
    scientific investigations to further the field of data mining.

    Candidates should have a recent Ph.D. in computer science, physics,
    mathematics, engineering, or the physical sciences. Candidates with
    experience developing software for large C/C++ codes, and with a
    background in probability and statistics, feature detection or related
    areas will be preferred.

    LLNL offers a challenging environment and a competitive
    salary/benefits package. We are located 45 miles Southeast of San
    Francisco, California. To apply send a cover letter with a resume to:
    Ms. Molly Dougan, Recruiting and Employment Division, PO Box 808,
    Dept. AIMIS18IN, L-155, Livermore, CA 94550 (dougan3@llnl.gov).
    Resumes will be reviewed and if there is interest, you will be
    contacted by the hiring department. We are proud to be an equal
    opportunity employer.

    For more information about the Center for Applied Scientific Computing
    visit our web site at: http://www.llnl.gov/CASC

    To view other employment opportunities at LLNL visit our web site at:
    http://www.llnl.gov

    Previous  10 Next   Top
    From: foster@Basit.COM (Foster Provost)
    Date: Fri, 30 Jan 1998 08:51:44 +0500
    Subject: REMINDER: KDD-98 paper submissions due in March

    This year's conference on Knowledge Discovery and Data Mining
    will be held in New York City (August 27-31).

    We encourage the submission of both fundamental and applied research
    papers on algorithms for discovery and for extracting knowledge from
    data, as well as papers on other facets of the knowledge discovery
    process (representation, visualization, evaluation, etc.).

    Please see http://www-aig.jpl.nasa.gov/public/kdd98/ for details.

    Due dates:

    March 10 - KDD-98 electronic title page
    March 17 - KDD-98 full papers

    Previous  11 Next   Top
    Date: Thu, 05 Feb 98 17:38:19 -0800
    From: 'Gen Topping'gtopping@mfi.com

    The Third Annual Data Mining Summit is coming up March 1-4 at the
    Beverly Hilton Hotel in Beverly Hills, California. It will be one of
    the year's top conferences in KDD, data mining, data visualization,
    and related decision support practices. The program is focused on
    practical application of data mining techniques for business
    intelligence.

    The Summit will feature case studies by GTE, Reliastar Life Insurance, Proctor &
    Gamble, and others. Presenters include Usama Fayyad, Gregory Piatetsky-Shapiro,
    Evangelos Simoudis, Kamran Parsaye, Foster Provost, Bob Evans, Sue Osterfelt,
    and Vasant Dhar. 'Innumeracy' author John Allen Paulos will keynote. The full
    program is up on the Web at
  • www.dbsummit.com.
    • Registration Hotline is (415)
    905-2267. The conference is produced by Database Programming & Design magazine
    and Miller Freeman Inc. We hope to see everyone in Southern California in March!



    Previous  12 Next   Top
    Date: Wed, 07 Jan 1998 13:15:56 +0100
    From: Melanie Hilario Melanie.Hilario@cui.unige.ch
    Subject: CFP: ECML'98 WS - Upgrading Learning to the Meta-Level

    Call for Papers
    ECML'98 Workshop

    UPGRADING LEARNING TO THE META-LEVEL:
    MODEL SELECTION AND DATA TRANSFORMATION

    To be held in conjunction with the
    10th European Conference on Machine Learning
    Chemnitz, Germany, April 24, 1997

    http://www.cs.bris.ac.uk/~cgc/ecml98-ws.html

    Motivation and Technical Description

    Over the past decade, machine learning (ML) techniques have successfully
    started the transition from research laboratories to the real world. The
    number of fielded applications has grown steadily, evidence that industry
    needs and uses ML techniques. However, most successful applications are
    custom-designed and the result of skillful use of human expertise. This is
    due, in part, to the large, ever increasing number of available ML models,
    their relative complexity and the lack of systematic methods for
    discriminating among them. Current data mining tools are only as
    powerful/useful as their users. They provide multiple techniques within a
    single system, but the selection and combination of these techniques are
    external to the system and performed by the user. This makes it difficult
    and costly for non-initiated users to access the much needed technology
    directly.

    <>

    This workshop is intended to bring together researchers who have attempted
    to use meta-level approaches to automate or guide decision-making at all
    stages of the learning process. One broad line of research is the static use
    of prior (meta-)knowledge. Knowledge-based approaches to model selection
    have been explored in both symbolic and neural network learning. For
    instance, prior knowledge of invariances has been used to select the
    appropriate neural network architecture for optical character recognition
    problems. Another research avenue aims at augmenting and/or refining
    meta-knowledge dynamically across different learning experiences.
    Meta-learning approaches have been attempted to automate model selection (as
    in VBMS and StatLog) as well as model arbitration and model combination (as
    in JAM). Contributions are sought on any of the above--or
    other--approaches
    from all main sub-fields of machine learning, including neural networks,
    symbolic machine learning and inductive logic programming.

    For full information please see the URL above.

    Previous  13 Next   Top
    From: nakhaeizadeh@dbag.ulm.DaimlerBenz.COM (Gholamreza Nakhaeizadeh)
    Date: Thu, 22 Jan 1998 10:22:05 +0100
    Subject: ECML 98 Workshop: Application of Machine Learning and Data Mining
    in Finance
    Web: http://www.tu-chemnitz.de/informatik/ecml98

    Workshop: Application of Machine Learning and Data Mining in Finance

    10th European Conference on Machine Learning (ECML-98)
    Chemnitz, Germany, April 24 1998
    http://www.tu-chemnitz.de/informatik/ecml98

    Motivation

    Advanced data analysis and forecasting technologies such as neural
    networks, symbolic machine learning and genetic algorithms are being
    increasingly applied to support financial asset management and credit
    risk management. These methods are considered by many financial
    management institutions as innovative technologies to support
    conventional quantitative techniques. Their use in computational finance
    will have a major impact in the modelling of the currency markets, in
    tactical asset allocation, bond and stock valuation and portfolio
    optimisation. In addition the application of these tools for scoring
    tasks delivers valuable support for the management of client credit
    risk.

    <>

    Research papers representing new and significant developments in
    methodology as well as applications of practical use will be
    presented. Topics include:

    Application aspects:

    - Scoring systems: Application and Behavioural Scoring
    - Trading- and forecasting models
    - Volatility models
    - Value at Risk
    - Financially motivated objective functions

    Methodological aspects:

    - Symbolic Learning in financial engineering
    - Neural Networks for financial applications
    - Aspects and dependencies of data transformation and model selection
    - Backtest procedures: Advantages and bottlenecks
    - Pre-testing as an alternative to backtest
    - Data Mining process model for financial applications

    For further information about the main conference and registration
    please contact:

    ecml98@lri.fr
    ecml98@informatik.tu-chemnitz.de

    or visit the web site: http://www.tu-chemnitz.de/informatik/ecml98

    Previous  14 Next   Top
    Date: Wed, 14 Jan 1998 17:37:26 +0000
    From: Vincent Corruble V.Corruble@abdn.ac.uk
    Subject: CFP: ECAI-98 WORKSHOP ON MACHINE DISCOVERY

    August 24, 1998
    Brighton, U.K
    Web: http://www.csd.abdn.ac.uk/~vcorrubl/disco98/cfp.html

    Scientific discovery is a human and social process that has attracted
    attention from a growing portion of the AI community, as well as from
    neighbouring disciplines such as philosophy and psychology. It is a
    privileged area for the study of creativity, itself a fundamental
    subject for Artificial Intelligence research. Though the field originated
    in the US, European research on machine discovery has known significant
    developments since the early 90's. The latest meeting dedicated to scientific
    discovery, held as a AAAI Spring Symposium at Stanford University in 1995,
    was successful, in large part because it brought together researchers from
    various disciplines, within and outside AI, with a shared interest. A new
    workshop organised in the same spirit will be held at the 1998 European
    Conference on Artificial Intelligence in Brighton, U.K.

    Contributions are invited in the following areas:
    - New systems and tools to automate or aid scientific discovery
    - New computational models of scientific activity
    - Reports on new scientific findings resulting from the use of computational
    tools performing non-trivial, high-end tasks
    - Lessons learned from earlier science (recent or not so recent) through
    computational simulations and case studies
    Throughout the workshop, demonstrations of working systems will be encouraged
    when appropriate.

    <>

    Contact for enquiries
    Derek Sleeman or Vincent Corruble. E-mail address: disco98@csd.abdn.ac.uk
    Workshop WWW page: http://www.csd.abdn.ac.uk/~vcorrubl/disco98/disco98.html
    ECAI official WWW page for workshop W9 : http://www.cogs.susx.ac.uk/ecai98/tw/W9.html
    ECAI-98 main WWW page: http://www.cogs.susx.ac.uk/ecai98/
    ECAI official WWW page for IDAMAP'98: http://www.cogs.susx.ac.uk/ecai98/tw/W21.html
    Previous  15 Next   Top
    Date: Mon, 19 Jan 1998 18:28:38 +0100
    From: Riccardo Bellazzi ric@ipvaimed3.unipv.it
    Subject: IDAMAP 98 announcement

    Inteligent Data Analysis in Medicine and Pharmacology
    A Workshop at the 13th European Conference on Artificial Intelligence
    http://aim.unipv.it/~ric/idamap98
    Call for Papers

    GENERAL INFORMATION

    IDAMAP-98, a one day ECAI-98 workshop, will be held in
    Brighton, UK, on Sunday, August 24, 1998 prior to the start of the
    main ECAI conference. This is the third workshop on Intelligent data
    analysis in medicine and pharmacology. The former IDAMAP Workshops
    were held at Budapest in 1996 and in Nagoya in 1997.

    Gathering in an informal setting, workshop participants will have
    the opportunity to meet and discuss selected technical topics in an
    atmosphere which fosters the active exchange of ideas among
    researchers and practitioners. To encourage interaction and a
    broad exchange of ideas, the workshop will be kept small,
    preferably under 30 participants and certainly under 40.
    Attendance will be limited to active participants only.
    Ample time will be allotted for general discussion.
    The workshop will last one full day.
    Attendees at the workshop will have to register for the main
    ECAI conference.

    <>

    DEADLINES

    April 15, 1998 Paper submission deadline
    May 5, 1998 Notification to Authors
    May 20, 1998 Camera-ready papers

    For further information, visit Workshop's Web page at
    http://aim.unipv.it/~ric/idamap98

    Previous  16 Next   Top