Knowledge Discovery Nuggets 97:20, e-mailed 97-06-16

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Knowledge Discovery Nuggets 97:20, e-mailed 97-06-16

News:
* Kamran Parsaye, Emerging Standards and the KDD Cup
* Alex Goodall, AI Information Bank now On-Line,
  • http://aiintelligence.com/

    • Publications:
    * Michael Berry, Book: Data Mining Techniques for Marketing, Sales and Customer Support,
  • http://www.data-miners.com

    • * Federico Pietro, KDD and Data Mining in Italian,
  • http://www.dei.unipd.it/~fontana/Reti/Tesina/kdddm.html

    • Siftware:
    * J. P. Brown, Superinduction Upgrade
  • http://www.hal-pc.org/~jpbrown

    • * Ronny Kohavi, Silicon Graphics' MineSet version 1.2
  • http://www.sgi.com/Products/software/MineSet

    • * Marco Ramoni, Software Available: Bayesian Knowledge Discoverer (Beta)
  • http://kmi.open.ac.uk/~marco/projects/bkd/software

    • Positions:
    * Yves Kodratoff, 'professor-researcher' in Laval, France
    * Ed Babb, Job in data mining,
  • http://www.parsys.com/datamine.htm

    • * Patricia Riddle, Datamining Studentship in New Zealand
  • http://www.cs.auckland.ac.nz/~cthombor/PhD/info.txt

    • Meetings:
    * David Leake, ICCBR-97 - 2nd Call for Participation,
  • http://www.iccbr.org/iccbr-97.html

    • * Eric Horvitz, UAI '97 Conference and Full-Day Course Programs
  • http://cuai97.microsoft.com

    • --
    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 3-4 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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    We've all heard that a million monkeys banging on a million typewriters
    will eventually reproduce the entire works of Shakespeare. Now, thanks
    to the Internet, we know this is not true.

    --Professor Robert Silensky
    (thanks to Sarah Hedberg for sending this)
    Previous  1 Next   Top

    [Kamran Parsaye asked me to include in KD Nuggets the following letter discussing
    his reservations about the KD Cup (see www.kdnuggets.com/kd-cup.txt).
    While I agree with his arguments that there is a lot more to Knowledge Discovery
    that clustering and classification, I think there is still significant value in the
    present competition, as indicated by the large number of participants who
    expressed interest in it. With lessons from KDD-Cup 97, a better competition
    could be devised in the future. GPS]

    Date: Tue, 10 Jun 1997 03:07:28 -0500 (CDT)
    TO: Gregory Piatetsky-Shapiro, Editor KD Nuggets
    FROM: Kamran Parsaye datamine@ix.netcom.com (IDI)
    RE: Emerging Standards and the KDD Cup
    DATE: June 9, 1997

    Following our email exchanges about the 1997 KDD-Cup,
    I assume you know that I have a number reservations about
    the completeness and consistency of the logic used in
    the KDD Cup-Document. As you pointed out in your message,
    the 1997 Cup itself may be seen as a passing issue, but I
    still think we should get the logic straight since it
    may give rise to some form of 'de facto industry standard'
    that may endure.

    The data mining industry is in its early stages of formation
    now and we should all strive for consistency and clarity.
    This is particularly important because data mining has a
    far more complicated theory than its other decision
    support counterparts -- OLAP and query processing.

    We all need to exercise caution when dealing with the
    fundamental issues of data mining and since KDD is a
    publicly respected organization, I would like to
    formally suggest a revision of the logic used in
    the Cup-Document as posted in the last issue of the
    KDD-Nuggets. I have sketched the beginnings of my
    'list of reservations' below, and I welcome your comments.

    The first issue is that the Cup-Document seems to equate
    knowledge discovery with simple attribute-based approaches
    to clustering. As you well know, this is far from complete
    or satisfactory. Towards the end of the document some
    references are made to associations, time-series and
    other issues, but not clearly enough to achieve
    consistency. I do agree with your comment that that
    dealing with these other patterns would have taken a
    lot of effort, but that does not change the fact that
    ignoring them will leave a large gap in logic.
    Affinity analysis, trend analysis, comparative analysis,
    etc. are all essential to discovery and in my opinion
    ignoring them is similar to selectively ignoring two
    thirds of the periodic-table of the elements in a basic
    review of chemistry. These patterns are as fundamental
    to the worlds of data and knowledge as the other two
    thirds of the periodic-table are fundamental to the
    world of the elements.

    The second (and effort-related) issue is that the
    Cup-Document has spent significant energy to deal
    with engineering details that should be considered as
    'absolute pre-requisites' and not as criteria.
    Of course, users expect that any reasonable system
    should deal with both numbers and constants, should
    access the database directly, and should run
    client-server, etc. These pre-requisites are absolutely
    necessary, but will become trivial in a year's time when
    everyone has gotten around to engineering them.
    Since they do not constitute a fundamental challenge,
    focusing on them takes away from the attention for
    the real issues. I suggest just listing them in an
    appendix as basic pre-requisites so the important
    issues can be clarified.

    The third issue is fundamental and has to do with
    the most important aspect of knowledge discovery --
    i.e. the need to produce 'correct' results.
    Practically speaking, this has become a really serious
    issue now that most of the world has discovered the
    need for multi-dimensionality in decision support.
    There is no question that much of the world's data
    is multi-dimensional -- as of last year, 100% of the
    Fortune 500 companies were using some form of a
    multi-dimensional data analysis system. And, as I
    showed in my last article in Database Programming
    and Design in February 1997
    (available at
  • http://www.dbpd.com/bestof.htm
    • a set
    of simple and widespread tables with about 20
    records each can quickly lead many well known
    data mining approaches to confusion.
    Hence 100% of the Fortune 500 companies are open to
    confusion with the Cup-Document. If a Fortune 500
    company can not trust the results it gets from a
    system from the analysis of a simple 20 record sales
    database anyway, there is little to discuss about
    the other issues.

    Talking about Fortune 500 companies, the fourth issue
    has to do with user-democracy. The Cup-Document, as is,
    seems to reflect an 'analyst's view' of the world and
    reads like a constitution written by-the-analyst and
    for-the-analyst. The mass of business users seem to
    have had no representative voice or vote in it --
    it would be a safe bet to assume that over 90% of
    the authors were analysts. This can be the topic of
    a lengthy discussion that deserves its own forum --
    but should not be ignored. This also implies a list
    of follow-on issues that have to do with
    democratic information distribution, the web, etc.

    The fifth issue is fundamental and has to do with
    'discovery power', as distinct from the first issue
    above. I think the concept of what a system can
    discover and tell a user about should receive more
    attention in the Cup-Document. The discovery power
    of a system (i.e. what the system can tell a user about)
    directly impacts the benefits the user will receive.
    Hence discovery power is one of the key issues for KDD
    and deserves as much attention as almost any other
    topic -- correctness coming first, of course.
    I will discuss this in more detail in a forthcoming article
    on Patterns of Knowledge that I will send you later, and
    will also later post at
  • http://www.datamining.com.


    • These five issues are just the beginnings of my
    'list of reservations' about the current version of
    the Cup-Document and a large number of other issues
    have not even been mentioned yet -- with due
    acknowledgment to the limitations of space and time.
    The fact that we could discuss each of these in far
    greater detail simply shows how much caution and
    depth is needed in dealing with data mining at
    a serious level.

    I do hope that this partial 'list of reservations' will
    begin a discussion that will shed light on the need for
    a rich context of discourse for knowledge discovery.

    Thanks,
    Kamran.

    Previous  2 Next   Top
    Date: Tue, 10 Jun 1997 19:25:49 +0100
    From: Alex Goodall (Alex@aiintelligence.com)
    Subject: AI Information Bank now On-Line

    **** Announcement ****

    Apologies if you receive mulitple copies of this from different sources.
    Please distribute as you see fit.

    THE AI INFORMATION BANK LAUNCHED

    9th June 1997
    -------------

    AI Intelligence is pleased to announce that the AI Information Bank is
    now available via

  • http://aiintelligence.com/


    • The Bank is set to become the most comprehensive resource on the Web
    covering commercial Artificial Intelligence (AI). It lists products and
    suppliers alphabetically, and includes pages covering specific
    technologies, such as:

    Knowledge-Based Systems, Data Mining, Neural Nets, Fuzzy Logic, Case-
    Based Reasoning, Genetic Algorithms and more.

    Many people we speak with are expressing the view that there is a
    resurgence of commercial interest in AI and its associated technologies.
    The timing for the launch of the Bank is therefore most appropriate.

    The AI Information Bank was conceived and designed by Alex Goodall and
    Charles Langley. It is being made available as a service from
    AI Intelligence - publisher of the AI Watch newsletter and the
    AI Perspectives reports.


    If you are a supplier wishing to have your information displayed
    in the Bank, please look at

  • http://aiintelligence.com/aii-info/aiib-reg.htm


    • Alex Goodall
    AI Intelligence PO Box 95 OXFORD OX2 7XL United Kingdom

    U:
  • http://aiintelligence.com/

    • E: alex@aiintelligence.com
    T: +44 1865-791 600
    F: +44 1865-791 007


    Previous  3 Next   Top
    Date: Sun, 08 Jun 1997 22:12:13 -0400
    From: 'Michael J. A. Berry' (mjab@ent.mrj.com)
    Subject: Data Mining Techniques for Marketing, Sales and Customer
    Support

    I believe that readers of this list may be interested in our recently
    published book, 'Data Mining Techniques for Marketing, Sales and Customer
    Support.' This book has just been published by John Wiley & Sons. The
    primary audience is the technically literate marketing manager, but there is
    much to interest data mining practitioners as well.

    For more information or to order the book, see
  • http://www.data-miners.com


    • -Michael Berry & Gordon Linoff

    ===================
    Michael J. A. Berry
    MRJ Technology Solutions

    mjab@naviant.com
    +1 617 591-3041

  • http://www.data-miners.com


    • Previous  4 Next   Top
    Date: Fri, 06 Jun 1997 15:12:24 +0200
    From: Federico Pietro 357382/IF (federic@dei.unipd.it)
    Subject: KDD and Data Mining in Italian

    We are a team of students at Padova University in Inforamtics
    Engineering, you may be interested in adding to KDD nuggets a work on
    Data Mining and Knoledge discovery in Italian we did for a course in


    Computer Networks. There is an overview on KDD techniques, on assocation
    rule discovery, episode matching and web mining in postscript format.
    There is also a list of links to pages and articles we used.

    try this link:

  • http://www.dei.unipd.it/~fontana/Reti/Tesina/kdddm.html


    • Thank You....

    ---------------------------
    Reply to:Federico Pietro
    mailto:federic@dei.unipd.it
    Web Page......
  • http://www.dei.unipd.it/~federic/


    • Previous  5 Next   Top
    [The following is a commercial announcement. GPS]
    Date: Thu, 22 May 1997 15:12:57 -0500
    From: 'J.P.Brown' (jpbrown@hal-pc.org)
    Subject: An Upgrade

    This is just a note to emphasize that there are some new ideas around.

    Neither the sterile manipulation of data files, nor the knee-jerk
    application of 19th Century statistical short cuts will produce
    management-friendly Conclusions and Recommendations.

    The two things that are most necessary to give aid and comfort to
    management are:

    1: Convincing evidence that the information from the past is going
    to be allowed to tell its tale, Objectively.

    2: Reassurance that effective and continuing efforts are being made
    to detect incipient Change.

    The goal of absolute objectivity may be too much to expect in the
    business world, but my basic principle is to use AutoClassification as
    the first step in Business Analysis. This is a process which uses raw
    data to 'predict' some key results (that you already know). The system,
    and the classification which does this successfully, can then be used to
    make real predictions.

    Of course, every system to be used in this way needs an on-going Change
    Alarm, so that necessary adjustments can be made. Management should
    appreciate a system that is continually checking itself, and which would
    also provide an objective early warning of any major disruption.

    This is all part of SuperInduction which can be seen at
  • http://www.hal-pc.org/~jpbrown
    • with more at another address if you are
    interested,
  • http://www.hal-pc.org/~jpbrown/hmpg3a.html


    • I would be glad to discuss (defend) this application.

    J.P.


    Previous  6 Next   Top
    Date: Sun, 8 Jun 1997 21:39:30 -0700
    From: Ronny Kohavi (ronnyk@starry.engr.sgi.com)
    Subject: Silicon Graphics' MineSet version 1.2

    Silicon Graphics' MineSet version 1.2 Increases Functionality
    with Web and PC Connectivity.
    -------------------------------------------------------------


    Silicon Graphics released MineSet(TM) version 1.2 on 3 June 1997.
    MineSet 1.2 is the newest version of its flagship data mining suite of
    integrated visual and analytical data mining tools. Users can now
    interact with the full power of MineSet from their PCs using
    OpenGL(R)-enabled X-servers, such as Hummingbird Communications'
    Exceed3D. MineSet 1.2 also enables web launching of MineSet tools on
    Silicon Graphics and PCs with OpenGL X-servers for easier interface
    and pre-canned mining operations. MineSet enables users to discover
    previously unknown patterns, hidden opportunities and trends by
    extracting information from data. MineSet automatically mines the
    data using powerful algorithms and allows analysis through intuitive,
    multi-dimensional visual tools.

    MineSet is unique in the industry because of its integration of data
    access, data transformation, data mining and visual data mining. The
    integration and ease-of-use measurably increases decision support
    productivity by bringing exploratory data analysis methods to
    analysts. Users such as brand managers, production managers, market
    development managers and data analysts are empowered with the ability
    to rapidly gain new insight, allowing them to easily transform
    consumer, demographic and industry data into actionable strategic
    decisions.

    MineSet 1.2 adds three new capabilities:

    1. Users can interact with the full power of MineSet through
    openGL-enabled X-servers. On PCs, such capability is provided
    by Hummingbird Communications's Exceed3D. Other workstation
    vendors provide similar X-server solutions for their platforms.

    2. MineSet visualizations can be launched through a web browser.
    The visualizations run natively on SGI platforms or through an
    OpenGL-enabled X-server.

    3. Users can script mining and visualization operations.
    The scripts can then be invoked by 3rd party applications.

    For more information, see
  • http://www.sgi.com/Products/software/MineSet

    • and download a 30-day free copy of MineSet (under more information).


    --

    Ronny Kohavi (ronnyk@sgi.com)
    Engineering Manager, Analytical Data Mining.



    Previous  7 Next   Top
    Date: Tue, 10 Jun 1997 11:46:27 +0100
    From: Marco Ramoni (M.Ramoni@open.ac.uk)
    Subject: Software Available: Bayesian Knowledge Discoverer (Beta)


    BAYESIAN KNOWLEDGE DISCOVERER

    Version 0.1 (Beta)


    This is to announce the availability of the Beta release of Bayesian
    Knowledge Discoverer (BKD) version 0.1.

    BKD is a program designed to extract Bayesian Belief Networks (BBNs)
    from (possibly incomplete) databases. It is based on a new estimation
    method called Bound and Collapse and its extensions to model
    selection.

    BKD 0.1b can be downloaded from:

  • http://kmi.open.ac.uk/~marco/projects/bkd/software


    • Program documentation is available from the same site.

    BKD 0.1b is available on the following platforms:


    MACINTOSH: Power Macintosh (System 7).

    UNIX: Sun Sparc Station (Solaris 2.4).
    Sun Sparc Station (Solaris 2.5.1).
    Sun Ultra (Solaris 2.5.1).

    Marco

    Previous  8 Next   Top
    Date: Fri, 6 Jun 1997 17:45:29 +0200 (MET DST)
    From: Yves.Kodratoff@lri.fr (Yves.Kodratoff@lri.lri.fr)

    We are looking for candidates to a position of 'professor-researcher' at
    ESIEA Group's research center. The position will be held in Laval a nice
    French provincial town situated in the center of Brittany.
    PhD requested, teaching in the French language compulsory, research on Data
    Mining.
    Both applied (expertise in data mining applied to problems of local
    industries) and pure research, particularly: text mining and/or computer
    security (= mining security backlogs).
    Research work performed under the leadership of Yves Kodratoff.

    Candidates should apply to the center's director: Mme A.M. KEMPF, ESIEA, 9,
    rue V sale, 75005 Paris, email: am.kempf@esiea.fr.

    Previous  9 Next   Top
    Date: 11 Jun 1997 16:04:15 +0000
    From: 'Ed Babb' (Ed_Babb@parsys.co.uk)
    Subject: JOB IN DATA MINING!

  • http://www.parsys.com/datamine.htm


    • JOB IN DATA MINING!

    PARSYS is a leading European supplier of parallel systems and technology. They
    are currently the lead partner in a large multinational ESPRIT project aimed at
    building a parallel data mining file server and client. They are looking for
    people interested in data mining systems and with experience of the enabling
    technologies of user interfaces, databases and machine learning. Knowledge of C
    programming is essential. Knowledge of PROLOG, JAVA and Visual Basic would be
    useful.

    At least a 2.1 degree in Computing, Artificial Intelligence or equivalent is
    needed. In addition, several years relevant experience is desirable. Salary up
    to 35K pounds depending on experience.

    Please post your CV stating current salary to: Ed Babb, PARSYS LTD, Boundary
    House, Boston Road, Hanwell, London, W7 2QE, UK. Alternatively email him on
    ed@parsys.co.uk if you wish to make any brief informal enquires.

    Please see
  • 'http://www.parsys.com/datamine.htm'
    • for information on data mining
    group.

    Previous  10 Next   Top
    Date: Fri, 13 Jun 1997 14:51:31 +1200 (NZST)
    From: Patricia Riddle (pat@cs.auckland.ac.nz)
    Subject: Datamining Studentship

    URL:
  • http://www.cs.auckland.ac.nz/~cthombor/PhD/info.txt


    • DEPARTMENT OF COMPUTER SCIENCE
    UNIVERSITY OF AUCKLAND

    Research Studentship in Knowledge Discovery and Datamining

    The Artificial Intelligence Group at the University of Auckland has a
    vacancy for a

    Research Assistant

    Applications are invited for an PhD studentship, within the Artificial
    Intelligence Group, at the Department of Computer Science, University
    of Auckland New Zealand. The three-year studentship is for the
    investigation of making intelligent data analysis techniques usable by
    novice data owners. The successful candidate will have a tax-free
    scholarship of NZ$12,000 dollars and will be expected to work on a
    research project on 'Novice Tools for Knowledge Discovery'. Student
    fees will also be covered for NZ, French, or German students. An
    additional teaching fellowship of NZ$4800 (taxable) might also be
    provided by the Computer Science Department.

    The AI Group at Auckland conducts research into constraint
    satisfaction, datamining, machine learning, planing, and spatial
    reasoning.

    Applicants should have at least a Masters in Computer Science or
    related subject, with a good background in Artificial Intelligence
    or Statistics. Please submit a CV as soon as possible, but
    not later than 31 July 1997, to

    Dr P Riddle,
    Department of Computer Science,
    University of Auckland,
    Private Bag 92019, Auckland,
    New Zealand.

    Phone Dr Riddle on (64) (9) 373-7599 (ext 7093), fax at (64) (9)
    373-7453 or send email to (pat@cs.auckland.ac.nz) if you wish to make
    an informal enquiry.

    General information on PHD studies at Auckland University can be found
    at:

  • http://www.cs.auckland.ac.nz/~cthombor/PhD/info.txt



    • Previous  11 Next   Top
    Date: Tue, 10 Jun 1997 16:16:49 +0200
    From: David Leake (leake@cs.indiana.edu) (by way of Enric Plaza i Cervera)
    Subject: ICCBR-97 - 2nd Call for Participation
    Second Call for Participation

    ICCBR-97
    Second International Conference on Case-Based Reasoning

    Brown University
    Providence, Rhode Island, July 25-27, 1997


    IMPORTANT DEADLINES:
    The regular registration deadline is June 18
    The conference hotel and dormitory room blocks will be held until June 24
    (Note that all hotel-style rooms at Brown are now sold out, but
    dormitory rooms are still available.)

    Additional information is available from
  • http://www.iccbr.org/iccbr-97.html

    • Questions should be sent to iccbr97@iccbr.org.

    Contents

    - Conference Overview
    - Registration Form
    - Schedule outline and list of accepted papers
    - Travel and Accommodations Information
    - Program Committee and Sponsors

    Previous  12 Next   Top
    From: Eric Horvitz (horvitz@MICROSOFT.com)
    Subject: UAI '97 Conference and Full-Day Course Programs
    Date: Friday, June 13, 1997 4:15 PM


    Thirteenth Conference on Uncertainty in Artificial Intelligence
    (UAI '97)

  • http://cuai97.microsoft.com


    • August 1-3, 1997
    Brown University
    Providence, Rhode Island, USA

    =============================================

    ** UAI '97 Conference Program **

    =============================================


    Thursday, July 31, 1997

    Conference and Course Registration 8:00-8:30am
  • http://cuai97.microsoft.com/register/reg.htm


    • Full-Day Course on Uncertain Reasoning 8:30-6:00pm
  • http://cuai97.microsoft.com/course.htm


    • _____________________________________________

    Friday, August 1, 1997

    Main Conference Registration 8:00-8:25am

    Opening Remarks
    Dan Geiger and Prakash P. Shenoy
    8:25-8:30am

    Invited talk I: Local Computation Algorithms
    Steffen L. Lauritzen
    8:30-9:30am

    Invited talk II: Coding Theory and Probability Propagation in Loopy
    Bayesian Networks
    Robert J. McEliece
    9:30-10:30am

    Break 10:30-11:00am

    ** Plenary Session I: Modeling
    11:00-12:00am

    Object-Oriented Bayesian Networks
    Daphne Koller and Avi Pfeffer
    (winner of the best student paper award)

    Problem-Focused Incremental Elicitation of Multi-Attribute Utility
    Models
    Vu Ha and Peter Haddawy

    Representing Aggregate Belief through the Competitive Equilibrium of a
    Securities Market
    David M. Pennock and Michael P. Wellman


    Lunch 12:00-1:30pm


    ** Plenary Session II: Learning & Clustering
    1:30-3:00pm

    A Bayesian Approach to Learning Bayesian Networks with Local Structure
    David Maxwell Chickering, David Heckerman, and Chris Meek

    Batch and On-line Parameter Estimation in Bayesian Networks
    Eric Bauer, Daphne Koller, and Yoram Singer

    Sequential Update of Bayesian Networks Structure
    Nir Friedman and Moises Goldszmidt

    An Information-Theoretic Analysis of Hard and Soft Assignment Methods
    for Clustering
    Michael Kearns, Yishay Mansour, and Andrew Ng


    ** Poster Session I: Overview Presentations
    3:00-3:30pm

    * Poster Session I
    3:30-5:30pm

    Algorithms for Learning Decomposable Models and Chordal Graphs
    Luis M. de Campos and Juan F. Huete

    Defining Explanation in Probabilistic Systems
    Urszula Chajewska and Joseph Y. Halpern

    Exploring Parallelism in Learning Belief Networks
    T. Chu and Yang Xiang

    Efficient Induction of Finite State Automata
    Matthew S. Collins and Jonathon J. Oliver

    A Scheme for Approximating Probabilistic Inference
    Rina Dechter and Irina Rish

    Limitations of Skeptical Default Reasoning
    Jens Doerpmund

    The Complexity of Plan Existence and Evaluation in Probabilistic Domains

    Judy Goldsmith, Michael L. Littman, and Martin Mundhenk

    Learning Bayesian Nets that Perform Well
    Russell Greiner

    Model Selection for Bayesian-Network Classifiers
    David Heckerman and Christopher Meek

    Time-Critical Action
    Eric Horvitz and Adam Seiver

    Composition of Probability Measures on Finite Spaces
    Radim Jirousek

    Computational Advantages of Relevance Reasoning in Bayesian Belief
    Networks
    Yan Lin and Marek J. Druzdzel

    Support and Plausibility Degrees in Generalized Functional Models
    Paul-Andre Monney

    On Stable Multi-Agent Behavior in Face of Uncertainty
    Moshe Tennenholtz

    Cost-Sharing in Bayesian Knowledge Bases
    Solomon Eyal Shimony, Carmel Domshlak and Eugene Santos Jr.

    Independence of Causal Influence and Clique Tree Propagation
    Nevin L. Zhang and Li Yan
    __________________________________________________________

    Saturday, August 2, 1997

    Invited talk III: Genetic Linkage Analysis
    Alejandro A. Schaffer
    8:30-9:30am

    ** Plenary Session III: Markov Decision Processes
    9:30-10:30am

    Model Reduction Techniques for Computing Approximately Optimal Solutions
    for Markov Decision Processes
    Thomas Dean, Robert Givan and Sonia Leach

    Incremental Pruning: A Simple, Fast, Exact Algorithm for Partially
    Observable Markov Decision Processes
    Anthony Cassandra, Michael L. Littman and Nevin L. Zhang

    Region-based Approximations for Planing in Stochastic Domains
    Nevin L. Zhang and Wenju Liu

    Break 10:30-11:00am

    * Panel Discussion: 11:00-12:00am

    Lunch 12:00-1:30pm


    ** Plenary Session IV: Foundations
    1:30-3:00pm

    Two Senses of Utility Independence
    Yoav Shoham

    Probability Update: Conditioning vs. Cross-Entropy
    Adam J. Grove and Joseph Y. Halpern

    Probabilistic Acceptance
    Henry E. Kyburg Jr.

    Estimation of Effects of Sequential Treatments By Reparameterizing
    Directed Acyclic Graphs
    James M. Robins and Larry Wasserman


    ** Poster Session II: Overview Presentations
    3:00-3:30pm

    * Poster Session II
    3:30-5:30pm

    Network Fragments: Representing Knowledge for Probabilistic Models
    Kathryn Blackmond Laskey and Suzanne M. Mahoney

    Correlated Action Effects in Decision Theoretic Regression
    Craig Boutilier

    A Standard Approach for Optimizing Belief-Network Inference
    Adnan Darwiche and Gregory Provan

    Myopic Value of Information for Influence Diagrams
    Soren L. Dittmer and Finn V. Jensen

    Algorithm Portfolio Design Theory vs. Practice
    Carla P. Gomes and Bart Selman

    Learning Belief Networks in Domains with Recursively Embedded Pseudo
    Independent Submodels
    J. Hu and Yang Xiang

    Relational Bayesian Networks
    Manfred Jaeger

    A Target Classification Decision Aid
    Todd Michael Mansell

    Structure and Parameter Learning for Causal Independence and Causal
    Interactions Models
    Christopher Meek and David Heckerman

    An Investigation into the Cognitive Processing of Causal Knowledge
    Richard E. Neapolitan, Scott B. Morris, and Doug Cork

    Learning Bayesian Networks from Incomplete Databases
    Marco Ramoni and Paola Sebastiani

    Incremental Map Generation by Low Cost Robots Based on
    Possibility/Necessity Grids
    M. Lopez Sanchez, R. Lopez de Mantaras, and C. Sierra

    Sequential Thresholds: Evolving Context of Default Extensions
    Choh Man Teng

    Score and Information for Recursive Exponential Models with Incomplete
    Data
    Bo Thiesson

    Fast Value Iteration for Goal-Directed Markov Decision Processes
    Nevin L. Zhang and Weihong Zhang
    __________________________________________________________

    Sunday, August 3, 1997

    Invited talk IV: Gaussian processes - a replacement for supervised
    neural networks?
    David J.C. MacKay
    8:20-9:20am

    * Plenary Session V: Applications of Uncertain Reasoning
    9:20-10:40am

    Bayes Networks for Sonar Sensor Fusion
    Ami Berler and Solomon Eyal Shimony

    Image Segmentation in Video Sequences: A Probabilistic Approach
    Nir Friedman and Stuart Russell

    Lexical Access for Speech Understanding using Minimum Message Length
    Encoding
    Ian Thomas, Ingrid Zukerman, Bhavani Raskutti, Jonathan Oliver, David
    Albrecht

    Perception, Attention, and Resources: A Decision-Theoretic Approach to
    Graphics Rendering
    Eric Horvitz and Jed Lengyel

    * Break 10:40-11:00am

    * Panel Discussion: 11:00-12:00am

    Lunch 12:00-1:30pm


    ** Plenary Session VI: Developments in Belief and Possibility
    1:30-3:00pm

    Decision-making under Ordinal Preferences and Comparative Uncertainty
    D. Dubois, H. Fargier, and H. Prade

    Inference with Idempotent Valuations
    Luis D. Hernandez and Serafin Moral

    Corporate Evidential Decision Making in Performance Prediction Domains
    A.G. Buchner, W. Dubitzky, A. Schuster, P. Lopes P.G. O'Donoghue, J.G.
    Hughes, D.A. Bell, K. Adamson, J.A. White, J. Anderson, M.D. Mulvenna

    Exploiting Uncertain and Temporal Information in Correlation
    John Bigham


    Break 3:00-3:30am


    ** Plenary Session VII: Topics on Inference
    3:30-5:00pm

    Nonuniform Dynamic Discretization in Hybrid Networks
    Alexander V. Kozlov and Daphne Koller

    Robustness Analysis of Bayesian Networks with Local Convex Sets of
    Distributions
    Fabio Cozman

    Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks

    Adrian Y. W. Cheuk and Craig Boutilier

    Nested Junction Trees
    Uffe Kjaerulff

    __________________________________________________________

    If you have questions about the UAI '97 program, contact the UAI '97
    Program Chairs, Dan Geiger and Prakash P. Shenoy. For other questions
    about UAI '97, please contact the Conference Chair, Eric Horvitz.

    UAI '97 Conference Chair

    Eric Horvitz (horvitz@microsoft.com)
    Microsoft Research, 9S
    Redmond, WA, USA
  • http://research.microsoft.com/~horvitz



    • UAI '97 Program Chairs

    Dan Geiger (dang@cs.technion.ac.il)
    Computer Science Department
    Technion, Israel Institute of Technology

    Prakash Shenoy (pshenoy@ukans.edu)
    School of Business
    University of Kansas
  • http://pshenoy@stat1.cc.ukans.edu/~pshenoy/



    • To register for UAI '97, please use the online registration form at:
  • http://cuai97.microsoft.com/register/reg.htm


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