--
Discovery in Databases (KDD) community, focusing on the latest research and
applications.
Submissions are most welcome and should be emailed,
with a subject line that describes the submission (not 'submission to nuggets')
kdd@gte.com.
Please include URL for more info when available.
To subscribe/unsubscribe, email to kdd-request@gte.com
a message containing
subscribe kdd
in the first line.
Nuggets frequency is approximately 3 times a month.
Back issues of Nuggets, information on data mining tools,
and much other information on Data Mining and Knowledge Discovery
is available at Knowledge Discovery Mine site http://info.gte.com/~kdd
-- Gregory Piatetsky-Shapiro (moderator)
********************* Official disclaimer ***********************************
* All opinions expressed herein are those of the writers (or the moderator) *
* and not necessarily of their respective employers (or GTE Laboratories) *
*****************************************************************************
~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Nam et ipsa scientia potestas est.
--Francis Bacon (translation from Latin in next Nuggets)
Previous1NextTop
>~~~Publications:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Tue, 12 Nov 1996 18:51:29 -0500
From: samy@rcsuna.gmr.com
(R. Uthurusamy CS/50)
Subject: Communications of ACM Special Issue on Data Mining and Knowledge Discovery
Association for Computing Machinery
Special Issue of the Communications of the ACM
November, 1996, Volume 39, Number 11
Data Mining and Knowledge Discovery in Databases
------------------------------------------------
Guest Editors:
Usama Fayyad, Microsoft Research
Ramasamy Uthurusamy, General Motors
Introduction
* Usama Fayyad, Microsoft Research
* Ramasamy Uthurusamy, General Motors
The KDD Process for Extracting Useful Knowledge from Volumes of Data
* Usama M. Fayyad, Microsoft Research
* Gregory Piatetsky-Shapiro, GTE Laboratories
* Padhraic Smyth, University of California, Irvine
Statistical Inference and Data Mining
* Clark Glymour, Carnegie Mellon University
* David Madigan, University of Washington
* Daryl Pregibon, AT&T Laboratories
* Padhraic Smyth, University of California, Irvine
Mining Business Databases
* Ronald J. Brachman, AT&T Laboratories
* Tom Khabaza, Integral Solutions Ltd
* Willi Kloesgen, GMD, Germany
* Gregory Piatetsky-Shapiro, GTE Laboratories
* Evangelos Simoudis, IBM Almaden Research Center
The Data Warehouse and Data Mining
* W. H. Inmon, Pine Cone Systems
Mining Scientific Data
* Usama Fayyad, Microsoft Research
* David Haussler, University of California, Santa Cruz
* Paul Stolorz, Jet Propulsion Laboratory
A Database perspective on Knowledge Discovery
* Tomasz Imielinski, Rutgers University
* Heikki Mannila, University of Helsinki, Finland
The World Wide Web: Quagmire or Gold Mine?
* Oren Etzioni, University of Washington
--------------------------------------------------------------------
Abstracts of these articles and an expanded list of references are at http://www.research.microsoft.com/research/datamine/acm-contents.htm
--------------------------------------------------------------------
[it is great to see that CACM, the largest and oldest computer science
publication, has published a special issue on data mining. GPS]
Previous2NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: Leo Breiman (leo@stat.Berkeley.EDU)
Date: Wed, 13 Nov 1996 10:55:34 -0800
Subject: Paper: OUT-OF-BAG ESTIMATION
OUT-OF-BAG ESTIMATION
Leo Breiman*
Statistics Department
University of California
Berkeley, CA. 94708
leo@stat.berkeley.edu
Abstract
In bagging, predictors are constructed using bootstrap samples from the
training set and then aggregated to form a bagged predictor. Each bootstrap
sample leaves out about 37% of the examples. These left-out examples can
be used to form accurate estimates of important quantities. For instance, they
can be used to give much improved estimates of node probabilities and node
error rates in decision trees. Using estimated outputs instead of
the observed
outputs improves accuracy in regression trees. They can also be used to give
nearly optimal estimates of generalization errors for bagged predictors.
People who have read previous versions of the SuperInduction Website, http://www.hal-pc.org/~jpbrown
and those who have responded, will be interested in the latest
additions. These include new portrayals of Complexity, more tips on
Classification, and detection of Change.
Research to solve apparent problems has been encouraged by feedback
and questions.
Previous4NextTop
>~~~Positions:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: Colin Shearer (colin@isl.co.uk)
Date: Mon, 4 Nov 96 18:27:31 GMT
To: kdd@gte.com
Subject: Job Ad: VP Business Development
Career Opportunity: VP Business Development
Integral Solutions Limited is the UK's leading data mining tools company.
ISL's award winning Clementine software has established its reputation and
market position in Europe and the Far East.
ISL is now launching a US affiliate, headquartered in King of Prussia, PA,
with full responsibility for the North American market. Building on existing
US sales, the successful candidate's job is to make the business happen. The
VP Business Development will lay the foundations for a Sales, Marketing,
Consulting and Development Team, and create and manage a network of
distributors and partners.
This is an opportunity to participate in the growth and success of an exciting
new venture.
Mail or fax your resume, explaining why you're the ideal candidate for this
role, to Linda Montgomery at ISL on +44 1256 63467, or email lindam@isl.co.uk.
Include salary history and expectations.
Date: 12 Nov 1996 16:30:17 +0000
From: 'Ed Babb' (Ed_Babb@parsys.co.uk)
Subject: JOB- DATA MINING!
JOB: DATA MINING!
Dear Colleague
This may be a rare opportunity to enter the fast growing and exciting field of
data mining!
There are two jobs below which might interest you. One is concerned with the
user interface and communications. The second concerns the learning and
database technology.
If you are not interested yourself, please consider passing on to a colleague
or putting on an appropriate university careers notice board.
REGARDS
ED BABB
*********************************************
EXCITING OPPORTUNITY 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. Consequently, they are
looking for two people interested in data mining systems and with experience of
the enabling technologies of user interfaces, databases and machine learning.
The two positions involve:
1. Building client applications and the communications software for a client
server system. Experience of working with customers, system testing and
mounting demonstrators is important. Experience of C is essential. Experience
of Visual Basic useful.
2. Adapting learning techniques such as rule induction, neural networks,
genetic algorithms to run on a parallel computer. Also helping to adapt an
existing database system to run on a parallel machine. Enthusiasm for producing
fast algorithms in C is essential.
For both positions at least a 2.1 degree in Computing, Artificial Intelligence
or equivalent is needed. In addition, several years relevant experience is
desirable. Salary will depend on age and 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. Previous6NextTop
>~~~Positions:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Thu, 7 Nov 1996 07:52:47 -0500
From: 'William A. Wallace' (wallaw@rpi.edu)
Subject: STATISTICS FACULTY OPENING at RPI
STATISTICS FACULTY OPENING IN
DEPARTMENT OF DECISION SCIENCES AND ENGINEERING SYSTEMS
A tenure-track Assistant Professorship in statistical computing is
available commencing in January 1997 or September 1997:
Responsible for teaching courses at the graduate and
undergraduate levels and undertaking research in
statistical computing and statistical methodology.
Possible areas of specialization include linear and non-
linear models, quality and reliability engineering, and
data mining. Strong interest in engineering applications
is required.
The position requires outstanding research and teaching potential
or record, commitment to statistics in an academic engineering
environment and excellent communication skills.
Decision Sciences and Engineering Systems is one of ten departments
within the School of Engineering. With 21 regular faculty members
and a number of affiliated faculty from other departments, the
department offers an undergraduate degree in industrial and
management engineering; master's degrees in operations research and
statistics, industrial and management engineering, and
manufacturing systems engineering; and a doctoral degree in
decision sciences and engineering systems. The department is
heavily involved in teaching within the core engineering program
and is home to Rensselaer's Statistical Consulting Center. The
department is responsible for some 150 undergraduate students, 100
masters students, and 40 doctoral students.
Rensselaer Polytechnic Institute, founded in 1824, is the nation's
oldest technological university. With an undergraduate and
graduate population of 6,000 students, Rensselaer is a co-
educational, non-sectarian university and multicultural university
located in New York State's Capital District, a thriving
metropolitan area within 200 miles of New York, Boston and
Montreal.
Interested individuals should send vitae and three letters of
reference to:
Professor M. Raghavachari, Chair, Search Committee
Department of Decision Sciences and Engineering Systems
Rensselaer Polytechnic Institute
110 8th Street
Troy, NY 12180-3590
FAX: 518-276-8227
Rensselaer is an affirmative action/equal opportunity employer.
Women and minorities are encouraged to apply.
please post 9/19/96 cand-96
Raghu
Previous7NextTop
>~~~Meetings:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: Jiawei Han (han@cs.sfu.ca)
Date: Tue, 5 Nov 1996 09:53:25 -0800 (PST)
Subject: SIGMOD'97 Data Mining Workshop: Call for Papers
Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD'97)
in cooperation with ACM-SIGMOD'97
Tucson, Arizona, May 11 (Tentative) 1997
==========================================
OBJECTIVES
Mining knowledge from large databases and data warehouses is a promising
research area, with high application potential due to the huge amounts of
data accumulated in databases, data warehouses, and other information
repositories. Data mining has attracted people from many different fields,
including database systems, data warehouses, machine learning, knowledge
acquisition, statistics, information retrieval, and data visualization.
In June 1996, we organized a SIGMOD workshop on research issues on data
mining and knowledge discovery. It was well attended and was widely
considered to be successful in creating a forum for database researchers
to exchange their research ideas and results in data mining. To continue
to provide such a forum, we are organizing the second workshop in cooperation
with SIGMOD.
FORMAT
The workshop will be held one day before the SIGMOD/PODS'97 conference.
The plan is to have a full-day workshop, consisting of invited talks,
paper presentation/discussion sessions, a system demo session, and a
panel discussion session. If there are a good number of submissions
and enough interest, we may organize a poster session in parallel with
a data mining system demonstration session.
TOPICS
Major topics of interest include but are not limited to:
Foundations/principles of data mining
Data mining methods and algorithms
Association, classification, and prediction
Concept description: characterization and discrimination
Trend/deviation analysis and outlier detection
Integration of data mining and data warehousing
Mining knowledge in multidimensional databases
Integration of deduction, induction, and OLAP
Statistics, probability and uncertainty in data mining
Interestingness of discovered patterns
Efficiency and scalability in data mining
Parallel and distributed mining algorithms
Languages and interfaces for data mining
Visual data mining and visualization in data mining
Data mining systems and implementations
Data mining toolkits and methodologies
Performance and benchmarks of data mining systems
Mining spatial, temporal, and multimedia data
Data mining in heterogeneous databases and WWW
Integrated discovery systems
Successful data mining application examples
New application challenges and requirements
Inadequacy of current data mining mechanisms
Security and social impact of data mining
Influence of data mining to the advances of database systems
SUBMISSION AND REVIEWS OF POSITION PAPERS and RESEARCH PAPERS.
Authors are invited to submit position papers (limited to 5 pages) and/or
short research papers (or extended abstracts) (limited to 10 pages) on the
above topics. WE ENCOURAGE ELECTRONIC SUBMISSIONS IN THE FORM OF POSTSCRIPT,
LATEX, ETC. but limited to the std 8.5x11 sized paper. If hardcopies
are submitted, five copies will be required. Each submitted paper will
be reviewed by at least three program committee members. Selected
papers from this workshop will be considered for a special issue of
the journal: 'Data Mining and Knowledge Discovery'.
PROGRAM COMMITTEE
Rakesh Agrawal, IBM Almaden Research Center, USA
Inderpal Bhandari, IBM T.J. Watson Research Center, USA
Nick Cercone, University of Regina, Canada
Ming-Syan Chen, National Taiwan University, Taiwan
David W. Cheung, University of Hong Kong, Hong Kong
Umeshwar Dayal, Hewlett-Packard Laboratories, USA
Usama M. Fayyad, Microsoft Research, USA
Brian Gaines, University of Calgary, Canada
Randy Goebel, University of Alberta, Canada
Jiawei Han, Simon Fraser University, Canada
Tomasz Imielinski, Rutger University, USA
Bala Iyer, IBM Database Technology Institute, USA
Daniel A. Keim, University of Munich, Germany
Willi Kloesgen, GMD, Germany
Hans-Peter Kriegel, University of Munich, Germany
Laks V.S. Lakshmanan, Concordia University, Canada
Hongjun Lu, National University of Singapore, Singapore
Heikki Mannila, University of Helsinki, Finland
Shinichi Morishita, IBM Tokyo Research Center, Japan
Shamkant B. Navathe, Georgia Institute of Technology, USA
Raymond Ng, University of British Columbia, Canada
Shojiro Nishio, Osaka University, Japan
Gregory Piatetsky-Shapiro, GTE Laboratories, USA
Wei-Min Shen, University of Southern California, USA
Ramakrishnan Srikant, IBM Almaden Research Center, USA
Shalom Tsur, Hitachi America Ltd., USA
Alexander Tuzhilin, New York University, USA
Jeffrey D. Ullman, Stanford University, USA
Philip S. Yu, IBM T.J. Watson Research Center, USA
Carlo Zaniolo, Univ. of California at Los Angeles, USA
ORGANIZING COMMITTEE
Jiawei Han, Simon Fraser University, Canada
Laks V.S. Lakshmanan, Concordia University, Canada
Raymond Ng, University of British Columbia, Canada
IMPORTANT DATES
Submissions Due: January 24, 1997
Acceptance Notice: March 14, 1997
Final Version due: April 11, 1997
Send a short abstract of at most 150 words in ascii to rng@cs.ubc.ca
by January 24, 1997.
Five hard copies or one electronic copy of the paper should be
submitted by January 24, 1997, to
Dr. Raymond Ng
Department of Computer Science
University of British Columbia
Vancouver, B.C., V6T 1Z4, Canada
rng@cs.ubc.ca
Previous8NextTop
>~~~Meetings:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Tue, 29 Oct 1996 11:33:07 +0200
From: dang@csa.CS.Technion.AC.IL
(Dan Geiger)
Subject: First Call For Papers
** U A I 97 **
THE THIRTEENTH ANNUAL CONFERENCE ON
UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
August 1-3, 1997
Providence, Rhode Island, USA
=======================================
Visit the UAI-97 WWW page at http://cuai97.microsoft.com/
CALL FOR PAPERS
The effective handling of uncertainty is critical in designing,
understanding, and evaluating computational systems tasked with
making intelligent decisions. For over a decade, the Conference on
Uncertainty in Artificial Intelligence (UAI) has served as the
central meeting on advances in methods for reasoning under
uncertainty in computer-based systems. The conference is the
annual international forum for exchanging results on the use of
principled uncertain-reasoning methods to solve difficult challenges
in AI. Theoretical and empirical contributions first presented
at UAI have continued to have significant influence on the
direction and focus of the larger community of AI researchers.
The scope of UAI covers a broad spectrum of approaches to automated
reasoning and decision making under uncertainty. Contributions to
the proceedings address topics that advance theoretical principles or
provide insights through empirical study of applications. Interests
include quantitative and qualitative approaches, and traditional as
well as alternative paradigms of uncertain reasoning. Innovative
applications of automated uncertain reasoning have spanned a broad
spectrum of tasks and domains, including systems that make autonomous
decisions and those designed to support human decision making through
interactive use.
We encourage submissions of papers for UAI-97 that report on advances
in the core areas of representation, inference, learning, and
knowledge acquisition, as well as on insights derived from building
or using applications of uncertain reasoning.
We also call for submissions of statements of open problems of wide
interest for a discussion in a plenary session (see details below).
Topics of interest include (but are not limited to):
>> Foundations
* Theoretical foundations of uncertain belief and decision
* Uncertainty and models of causality
* Representation of uncertainty and preference
* Generalization of semantics of belief
* Conceptual relationships among alternative calculi
* Models of confidence in model structure and belief
>> Principles and Methods
* Planning under uncertainty
* Temporal reasoning
* Markov processes and decisions under uncertainty
* Qualitative methods and models
* Automated construction of decision models
* Abstraction in representation and inference
* Representing intervention and persistence
* Uncertainty and methods for learning and data mining
* Computation and action under limited resources
* Control of computational processes under uncertainty
* Time-dependent utility and time-critical decisions
* Uncertainty and economic models of problem solving
* Integration of logical and probabilistic inference
* Statistical methods for automated uncertain reasoning
* Synthesis of Bayesian and neural net techniques
* Algorithms for uncertain reasoning
* Advances in diagnosis, troubleshooting, and test selection
>> Empirical Study and Applications
* Empirical validation of methods for planning, learning, and
diagnosis
* Enhancing the human--computer interface with uncertain reasoning
* Uncertain reasoning in embedded, situated systems (e.g., softbots)
* Automated explanation of results of uncertain reasoning
* Nature and performance of architectures for real-time reasoning
* Experimental studies of inference strategies
* Experience with knowledge-acquisition methods
* Comparison of repres. and inferential adequacy of different calculi
* Uncertain reasoning and information retrieval
For papers focused on applications in specific domains, we suggest
that the following issues be addressed in the submission:
- Why was it necessary to represent uncertainty in your domain?
- What are the distinguishing properties of the domain and problem?
- What kind of uncertainties does your application address?
- Why did you decide to use your particular uncertainty formalism?
- What theoretical problems, if any, did you encounter?
- What practical problems did you encounter?
- Did users/clients of your system find the results useful?
- Did your system lead to improvements in decision making?
- What approaches were effective (ineffective) in your domain?
- What methods were used to validate the effectiveness of the
systems?
=================================
SUBMISSION AND REVIEW OF PAPERS
=================================
Papers submitted for review should represent original, previously
unpublished work (details on policy on submission uniqueness are
available at the UAI 97 www homepage). Submitted papers will be
evaluated on the basis of originality, significance, technical
soundness, and clarity of exposition. Papers may be accepted for
presentation in plenary or poster sessions. All accepted papers will
be included in the Proceedings of the Thirteenth Conference on
Uncertainty in Artificial Intelligence, published by Morgan Kaufmann
Publishers. An outstanding student paper will be selected for special
distinction.
Submitted papers must be at most 20 pages of 12pt Latex article style
or equivalent (about 4500 words). See the UAI-97 homepage for
additional details about UAI submission policies.
We strongly encourage the electronic submission of papers. To submit
a paper electronically, send two email messages to the program chairs at
uai97@cs.technion.ac.il
The first message includes the following information (in this order):
* Paper title (plain text)
* Author names, including student status (plain text)
* Surface mail and Email address for a contact author (plain text)
* A short abstract including keywords or topic indicators (plain
text)
The second message includes an electronic version of the paper
(Postscript format). The subject line of the second message should
be: $.ps, where $ is an identifier created from the last name of
the first author, followed by the first initial of the author's
first name. Multiple submissions by the same first author should be
indicated by adding a number (e.g., pearlj2.ps) to the end of the
identifier. Authors will receive electronic confirmation of the
successful receipt of their articles.
Authors unable to submit papers electronically should send the first
four items electronically to the email address above, and 5 copies of
the complete paper to one of the Program Chairs at the addresses
listed below.
=================================
SUBMISSION OF CHALLENGING PROBLEMS
=================================
This year we plan to hold an experimental plenary session entitled
'Challenging Problems in Uncertain Reasoning' to discuss critical open
problems. We request that interested researchers submit a description
of a critical open problem of wide interest that they consider
relevant to UAI (according to the guidelines for regular papers). The
submission should include a clear unambiguous statement of the
problem, ideas on possible solutions, and a survey of the relevant
literature where applicable. The statement should be no more than four
pages in length. Problems selected by the program chairs will be
presented in a plenary session. For each problem selected, the author
will give a concise presentation of the problem and its prospective
solutions. Following the brief presentations, there will be open
discussion of the problem and potential solutions with the entire
audience. Although the Challenging Problems will not appear in the
proceedings, proposals will be posted on the UAI '97 web pages by May
15 to allow participants to study them and to interact with the
authors.
++++++++++++++++++++++++++++++
Important Dates
++++++++++++++++++++++++++++++
>> All submissions must be received by: Feb 23, 1997
>> Notification of acceptance on or before: April 11, 1997
>> Camera-ready copy due: May 9, 1997
==========================
Program Cochairs (submissions and program inquiries):
=================
Dan Geiger
Computer Science Department
Technion, Israel Institute of Technology,
Haifa, 32000,
Israel
The Tenth Annual Conference on Computational Learning Theory (COLT'97)
will be held at Vanderbilt University in Nashville, Tennessee from
Sunday, July 6 through Wednesday, July 9, 1997. COLT'97 is sponsored
by Vanderbilt University, with additional support from AT&T Labs, and
in cooperation with ACM SIGACT and SIGART.
The conference will be co-located with the Fourteenth International
Conference on Machine Learning (ICML'97) which will be held Tuesday,
July 8 through Saturday, July 12. We anticipate a lively program
including oral presentations, posters, a number of invited speakers
and a half day of tutorials (jointly organized with ICML).
We invite papers in all areas that relate directly to the analysis of
learning algorithms and the theory of machine learning. Some of the
issues and topics that have been addressed in the past include:
* design and analysis of learning algorithms;
* sample and computational complexity of learning specific model
classes;
* frameworks modeling the interaction between the learner, teacher
and the environment (such as learning with queries, learning
control policies and inductive inference);
* learning using complex models (such as neural networks and
decision trees);
* learning with minimal prior assumptions (such as mistake-bound
models, universal prediction, and agnostic learning).
We strongly encourage submissions from all disciplines engaged in
research on these and related questions. Examples of such fields
include computer science, statistics, information theory, pattern
recognition, statistical physics, inductive logic programming,
information retrieval and reinforcement learning. We also encourage
the submission of papers describing experimental results that are
supported by theoretical analysis.
ABSTRACT SUBMISSION:
Authors are encouraged to submit their abstracts electronically.
Instructions for how to submit papers electronically can be obtained
after December 1 by sending email to colt97@research.att.com
with
subject 'help', or from our web page.
Alternatively, authors may submit fourteen copies (preferably
two-sided) of an extended abstract to:
Robert Schapire -- COLT'97
AT&T Labs
600 Mountain Avenue, Room 2A-424
Murray Hill, NJ 07974 USA
Telephone (for overnight mail): (908) 582-4533
Abstracts (whether hard-copy or electronic) must be RECEIVED by
11:59pm EST on
FRIDAY, JANUARY 17, 1997.
This deadline is FIRM. (We also will accept abstracts sent via air
mail and postmarked by January 6, or sent via overnight carrier by
January 16.) Authors will be notified of acceptance or rejection on
or before March 24, 1997. Final camera-ready papers will be due by
April 18.
Papers that have appeared in journals or other conferences, or that
are being submitted to other conferences (including ICML), are NOT
appropriate for submission to COLT.
ABSTRACT FORMAT:
The extended abstract should consist of a cover page with title,
authors' names, postal and email addresses, and a 200-word summary.
The body of the abstract should be no longer than 10 pages with at
most 35 lines per page, at most 6.5 inches of text per line, and in
12-point font. If the abstract exceeds 10 pages, only the first 10
pages may be examined. The extended abstract should include a clear
definition of the theoretical model used and a clear description of
the results, as well as a discussion of their significance, including
comparison to other work. Proofs or proof sketches should be
included.
PROGRAM FORMAT:
All accepted papers will be presented orally, although some or all
papers may also be included in a poster session. At the discretion of
the program committee, the program may consist of both long and short
talks, corresponding to longer and shorter papers in the proceedings.
By default, all papers will be considered for both categories.
Authors who DO NOT want their papers considered for the short
category should indicate that fact in a cover letter.
PROGRAM CHAIRS:
Yoav Freund and Robert Schapire (AT&T Labs).
PROGRAM COMMITTEE:
Andrew Barron (Yale University), John Case (University of Delaware),
Sally Goldman (Washington University), David Helmbold (University of
California, Santa Cruz), Rob Holte (University of Ottawa), Eyal
Kushilevitz (Technion), Ga`bor Lugosi (Pompeu Fabra University,
Barcelona), Arun Sharma (University of New South Wales), John
Shawe-Taylor (University of London), Satinder Singh (University of
Colorado, Boulder), Haim Sompolinsky (Hebrew University), Volodya Vovk
(Royal Holloway, University of London).
CONFERENCE AND LOCAL ARRANGEMENTS CHAIR:
Vijay Raghavan (Vanderbilt University).
STUDENT TRAVEL:
We anticipate some funds will be available to partially support travel
by student authors. Details will be distributed as they become
available.
TUTORIALS:
The program will include a half day of tutorials, jointly organized by
COLT and ICML, and intended as introductions to topics in the theory
and practice of machine learning. For further information, or to
submit a proposal for a tutorial, contact Sally Goldman, the tutorials
chair, at sg@cs.wustl.edu
or visit our web page.
Previous10NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Sun, 3 Nov 1996 23:19:40 -0600
From: 'Eric A. King' (eric@ahcsun1.heuristics.com)
Subject: Course
The Gordian Institute announces a new scheduling of it's popular course aimed
at data mining and pattern recognition entitled 'Making Sense of Data:
Computer-Aided Pattern Discovery.' The new offering will take place near
Pittsburgh at a resort in Wheeling, WV from January 27 through 31, 1997.
Registration for this first-rate, intenseive training course is $1995.
Academic and multi-seat pricing is available. You may review a detailed
description of the course at: www.heuristics.com/gordian.
At this site, you may review descriptions of other courses in the fields of:
* Data Mining
* Adaptive Machine Learning
* Intelligent Decision Systems
* Knowledge Engineering
* Hybrid Techniques
If you do not have convenient access to the web, a text copy of the course
description follows. Should you decide to attend The Gordian Institute's most
popular course, I encourage you to secure your seat early. Registration is a
snap via any of the following:
* Toll Free: 800-405-2114
* Fax: 304-547-4203
* Email: gordian@heuristics.com
* Web: www.heuristics.com/gordian/Register.html
* Cut & Paste Form: At end of this posting
Feel free to contact me at (gordian@heuristics.com)
with any questions
regarding this course, or any others on Gordian's curriculum.
Making Sense of Data: Computer Aided Pattern Discovery
Next Offering: January 27-31, 1997
Registration: $1995
Location: Oglebay Resort & Conference Center
Wheeling, WV; Convenient from Pittsburgh Airport
Lodging: Details faxed upon registration
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