--
Data Mining and Knowledge Discovery community,
focusing on the latest research and applications.
Contributions are most welcome and should be emailed,
with a DESCRIPTIVE subject line (and a URL, when available) to (kdd@gte.com).
E-mail add/delete requests to (kdd-request@gte.com).
Nuggets frequency is approximately weekly.
Back issues of Nuggets, a catalog of S*i*ftware (data mining tools),
and a wealth of other information on Data Mining and Knowledge Discovery
is available at Knowledge Discovery Mine site, URL http://info.gte.com/~kdd.
-- Gregory Piatetsky-Shapiro (moderator)
********************* Official disclaimer ***********************************
* All opinions expressed herein are those of the writers (or the moderator) *
* and not necessarily of their respective employers (or GTE Laboratories) *
*****************************************************************************
~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Predicting the future is easy.
Getting it right is the hard part.
Howard Frank, Director of IT Office at DARPA Previous1NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Thu, 20 Jun 1996 13:48:27 -0400
From: Gregory Piatetsky-Shapiro (gps0@gte.com)
Subject: MasPar takes on new name and softer edge 6/17/96
X-Url: http://www.computerworld.com/search/AT-html/9606/960617SL24maspar.html
MasPar takes on new name and softer edge
Dan Richman
06/17/96
Say good-bye to MasPar Computer Corp. and say hello to NeoVista
Solutions, Inc., the most recent entrant in the data mining field.
Cupertino, Calif.-based MasPar, which in its eight years sold 260
massively parallel machines to military, scientific and engineering
sites, last week changed its name and mission, quitting the hardware
business to become a provider of data mining software.
The shift offers MasPar a fresh start and users an alternative to
competitors' data mining software, industry observers said.
NeoVista's Decision Series data mining product is based on
pattern-recognition algorithms that MasPar developed with end users of
its hardware. Designed for applications such as fingerprint
recognition or radar detection, those algorithms were easily adapted
to data mining, NeoVista CEO John Harte said.
Data mining refers to discovering unknown patterns within
data. It is antithetical to conventional querying of data, which seeks
specific answers to specific questions.
Beta user Denise Barnhart, chief of corporate analysis at Army
Air Force Exchange, said Decision Series will permit her organization
to examine sales trends at a much finer level. Army Air Force Exchange
stocks 15,000 stores at bases throughout the world.
'To estimate sales based on buying patterns and demographics, we
look at sales by stores and region today,' she said. 'We think
NeoVista will let us drop down to the category or even the product
level.'
The new software, expected to ship Oct. 1, will compete most closely
with Intelligent Miner from IBM and the Database Mining products from
HNC Software, Inc. in San Diego.
'[This] breathes new life into MasPar, which flourished in the
Cold War days but faded as its proprietary technology couldn't find
more generalized uses,' said Jeff Liebl, a research analyst at Smaby
Group, Inc. in Minneapolis.
Previous2NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Thu, 13 Jun 96 10:08:54 -0700
From: Essam Yassa (samjess1@ix.netcom.com)
Subject: ai
Hello
I am looking for a real time system ( program) that can work with my data:
the data are in groups of a , b, c, ... etc. , about 500,000 or more groups.
- for each group, there is life values being feed into it in real time .
- some data in time series relate to real time ( now ) and some data represent values for different segments of time in the past and some for different segments of time into the future .
- I need a real time system that helps in making designs and prediction ( in real time ) for any data or values of my choice in any group, by learning and pattern recognizing the historical data for different groups and find non linear relationship in data , or between different groups
some of the groups can have some kind of interlined relationships.
Some of the groups have time existence and some are on going for life...
- it must also support DDE, AND DLL
thank you very much ...
Sam Jesse
samjess1@ix.netcom.com
859 N. MOUNTAIN AVE # 1B
UPLAND CA 91786
Previous3NextTop
>~~~Siftware:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Fri, 07 Jun 1996 15:00:23 -0700
From: Steve Champion (steve@neovista.com)
Organization: NeoVista Solutions
Subject: Submitting company and software for listing
NeoVista Solutions, Inc. Description
*Name: NeoVista Solutions, Inc
*URL: http://www.neovista.com
*Description: NeoVista provides the Global 2000 business solutions that
combine the Decision Series integrated suite of scalable data mining
tools with Professional Services that can be assembled into powerful,
automated predictive business analysis solutions for decision support.
*Discovery methods: Neural Nets, Clustering, Genetic Algorithms,
Association Rules
*Comments: NeoVista specializes in advanced pattern detection techniques,
having deployed highly parallel computing solutions in some of the most
demanding, defense related environments in the world, where accuracy and
speed are essential. Now, NeoVista's Decision Series software suite
brings the same advanced technology to commercial data mining, allowing
Global 2000 organizations to better understand the patterns of their
business. These solutions can be deployed on scalable, parallel platforms
that are available from accepted, standard hardware providers and operate
against data resident in popular databases or in legacy systems. The data
mining solutions from NeoVista thereby augment existing decision support
environments by integrating with installed, standards based systems.
*Platform(s): HP, SUN, DEC, Oracle, Informix, Sybase
*Contact: Marketing, NeoVista, info@neovista.com,
408-777-2929,
408-777-2930, 10710 North Tantau Ave, Cupertino, CA 95014
*Status: product and services
*Source of information: S. Champion, Neovista, steve@neovista.com
*Updated: 1996-06-07 by S. Champion, steve@neovista.com
Previous4NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: Iztok.Savnik@ijs.si
(Iztok Savnik)
Subject: Announcement: FDEP program
Date: Fri, 21 Jun 1996 10:09:44 +0200 (MET DST)
Program for Inducing Functional Dependencies from Relations
The program FDEP induces a set of functional dependencies from a given
input relation. By default, the output of the program is a set of
dependencies from which one can derive all functional dependencies
valid for a given relation.
The program uses the bottom-up technique of computing the functional
dependencies: it first computes the set of invalid functional
dependencies and after, using this set, determines the set of valid
functional dependencies. The candidates for the valid functional
dependencies are enumerated from more general to more specific
functional dependencies. The validity of the enumerated dependencies is
tested against the set of invalid dependencies.
*Platform(s):
FDEP is implemented in GNU C (v2.6). The program has been tested
on SPARC Station 10 running SunOS 4.1.3_U1.
*Contact:
Iztok Savnik (iztok.savnik@ijs.si)
at J.Stefan Institute, Slovenia
*Status: public domain, version 1.0 beta
Previous5NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Mon, 10 Jun 96 12:53:10 -0500 (CDT)
From: Melinda Conkling (melinda@airmail.net)
Subject: SIFTWARE information
=B7 Description
STATlab is an interactive, user-friendly exploratory data analysis software
for drilling-down into data and performing a multitude of analyses on that
data. STATlab allows any information user -- executive, managers,
researchers, students -- to import data from common formats such as a data
warehouse, relational databases, ASCII files, popular spreadsheets and most
of the popular statistical data systems, and interact with and manage the
data to perform a multitude of analyses.
=B7 Comments:=20
slp InfoWare is the only U.S. vendor offering an integrated data warehousing
solution, including a multi-dimensional database, data mining & drill-down,
statistical analysis and reporting tools in a client/server architecture.
=B7 Platform(s): Unix, Windows, DOS, Mac=20
Windows 95, OS/2, Windows 3.1, Windows NT, UNIX Motif, Macintosh
=B7 Status: public domain, product, prototype
Available now. STATlab=92s suggested retail price ranges from $300 for
STATlab to $600 for STATlabPRO, depending upon the module purchased.
=B7 Updated: 1996-06-10 by Melinda Conkling, melinda@airmail.net.
Previous6NextTop
>~~~Positions:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: sma@uhc.com
(Mack Amin)
Subject: Open Position
Date: Tue, 18 Jun 1996 12:54:43 -0500 (CDT)
The Advanced Technology Center of United HealthCare Corp. (UHC)
has a senior opening (with emaphasis on data warehousing, VLDB,
data mining) as described below. If you have appropriate background,
please send your resume to the given addresses.
In case of any questions, email me at sma@uhc.com.
o Coordinate ongoing technology assessment efforts throughout UHC
by leveraging the knowledge and experience of experts throughout
the corporation.
o Play a major role on implementation team addressing data and
systems integration, technology architecture, operations, and
change management.
o Establish and maintain relationships with key IT personnel, both
management and technical within UHC as well as with major vendor
representatives.
o Keep abreast of the latest technology advancements, make proposals
for analyzing new technologies, evaluate technology upgrades, and
consult on use.
o Actively participate in the various Technology Task Force and
Special Interest Group initiatives and provide leadership when
required.
II. Position Qualifications:
o MA or MS degree in Computer Science, MIS or equivalent experience
preferred.
o 7+ years experience in MIS related area (e.g. database design/
administration, data warehousing/decision support, software design/
development/support, hardware design/engineering/support, data
modeling, case tools, telecommunications, PC workstations, LAN/WAN
design/management, parallel computing, distributed domputing,
knowledge based/expert systems design/development, etc.) with
2+ years experience in data mining, VLDB, parallel DBMS, or data
warehousing.
o Large project management/consulting experience whose product/service
affected multiple organizations requiring cooperative involvement
is very desirable.
III. Special Skills:
o Must be a strong team player with excellent oral and written
communications skills.
o Must be able to act as a facilitator, integrator, and mediator with
people from various organizations having different interests, needs,
experiences, and expectations.
o Possesses strong planning, time management, analytical, and
organizational skills plus understand the relationship between
business needs and technical solutions.
o Have the ability to manage multiple tasks, prioritize work, work
independently and be self starting and self directed
Please send your resume (email/fax/snail) to any of the following
individuals:
Mack Amin Mike Biele
9705 Data Park, MN06-6130 9705 Data Park, MN06-613
Minnetonka, MN 55343 Minnetonka, MN 55343
Fax: 612-945-6502 Fax: 612-945-6502
Email: sma@uhc.com
Email: mbiele@uhc.com
Previous7NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Mon, 17 Jun 96 10:44 PDT
From: compfin@cse.ogi.edu
(Computational Finance)
Subject: New Computational Finance Program
Computational Finance at the Oregon Graduate Institute of
Science & Technology (OGI)
A Concentration in the MS Programs of
Computer Science & Engineering (CSE)
Electrical Engineering & Applied Physics (EEAP)
----------------------------------------------------------------
20000 NW Walker Road, PO Box 91000, Portland, OR 97291-1000
----------------------------------------------------------------
Program Overview
Today's technology has increased the level of technical pro-
ficiency required in the financial markets. At one time, for
example, spreadsheet skills and a good understanding of
financial instruments were all that were needed to build
practical derivative pricing tools. Today, leading edge
financial institutions routinely use advanced techniques
from engineering and computer science computers to create,
price, and manage risk for new instruments.
Computers of today are powerful enough to analyze and make
decisions based on real-time data (tick-by-tick). Further-
more, modern data analysis tools can consider many variables
simultaneously and capture complicated and often nonlinear
inter-dependencies between variables. This has opened up new
modeling possibilities for hedging, derivatives instruments,
and decision making.
At OGI, the demand within the financial industry for techni-
cally competent graduates that are well versed in state-of-
the-art analysis techniques is addressed by an intensive 12
month Computational Finance program. The program is offered
as a concentration in both the Computer Science & Egnineer-
ing (CSE), and Electrical Engineering & Applied Physics
(EEAP) departments. The program leads to a Master of Science
degree in Computer Science and Engineering (CSE track), or
in Electrical Engineering (EEAP track). Computational
Finance courses are also cross-listed in the Management of
Science & Technology (MST) program.
-------------------------------------------------------------------
The Computational Finance Program is:
------------------------------------------------------------------
* An intensive 12 month track directed at training scen-
tists, engineers, and financial professionals in the theory
and practice of advanced quantitative financial analysis.
* An attractive alternative to a standard 2 year MBA, for
highly motivated and technically sophisticated students
who are considering a career in quantitative financial
analysis.
* A way of gaining a solid foundation in finance, cover-
ing material equivalent to 3 semesters of MBA level
finance and beyond, in only 3 quarters. Finance courses
are taken in parallel with demanding engineering and
computer science courses, and the program is not
recommended for students without mathematical skills
corresponding to an undergraduate degree in a scientific
or technical field.
* Based on a solid foundation in relevant data analysis and
signal processing techniques from Computer Science,
Electrical Engineering, and Statistics. These techniques
are utilized for modeling financial markets and developing
investment analysis, trading, and risk management systems.
* Geared towards adaptive and nonlinear signal processing
tools, like artificial neural networks.
* Strongly project oriented, using state-of-the-art com-
puting facilities and live/historical financial market data
provided by Dow Jones Telerate. Students are exposed to tools
and trading environments that reflect the modern facilities
at a typical Wall Street trading firm. Students are also
trained in using high level numerical, and analytical
packages, such as MatLab, Mathematica, SPlus, and Expo, for
analyzing and modeling financial time series.
Admission requirements are the same as the general require-
ments of the institution. GRE scores are required for the
12-month concentration in Computational Finance, although
they can be waived under certain circumstances.
A candidate must hold a bachelor's degree in computer sci-
ence, engineering, mathematics, statistics, one of the bio-
logical or physical sciences, finance, or one of the quanti-
tative social sciences.
-------------------------------------------------------------------
Contact Information
------------------------------------------------------------------
For more information, contact
Program Information Admission Information
E-mail: CompFin@cse.ogi.edu
Betty Shannon, Academic
WWW: Coordinator http://www.cse.ogi.edu/CompFin/
Computer Science and
Engineering Department
Oregon Graduate Institute
of Science and Technology
P.O.Box 91000
Portland, OR 97291-1000
Previous8NextTop
>~~~Meetings:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Tue, 18 Jun 1996 17:00:52 -0700 (PDT)
From: Eric Horvitz (horvitz@cs.washington.edu)
Subject: UAI-96 program and registration information (fwd)
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 a 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.
* * *
UAI 96 events include a full-day course on uncertain reasoning on the
day before the main UAI 96 conference (Wednesday, July 31) at Reed
College. Details on the course are available at: http://cuai-96.microsoft.com/tutor.htm
* * *
On Sunday, August 4, we will hold a UAI-KDD Special Joint Session
on Learning, Probability, and Graphical Models at the
Portland Convention Center. See information on the program below.
Plenary Session I: Perspectives on Inference
8:45-10:15am
Toward a Market Model for Bayesian Inference
D. Pennock and M. Wellman
A unifying framework for several probabilistic inference algorithms
R. Dechter
Computing upper and lower bounds on likelihoods in intractable networks
T. Jaakkola and M. Jordan (Outstanding Student Paper Award)
Query DAGs: A practical paradigm for implementing belief-network
inference
A. Darwiche and G. Provan
Break 10:15-10:30am
Plenary Session II: Applications of Uncertain Reasoning
10:30-12:00am
MIDAS: An Influence Diagram for Management of Mildew in Winter Wheat
A. Jensen and F. Jensen
Optimal Factory Scheduling under Uncertainty using Stochastic
Dominance A*
P. Wurman and M. Wellman
Supply Restoration in Power Distribution Systems --- A Case Study in
Integrating Model-Based Diagnosis and Repair Planning
S. Thiebaux, M. Cordier, O. Jehl, J. Krivine
Network Engineering for Complex Belief Networks
S. Mahoney and K. Laskey
* Panel Discussion: 'Reports from the front: Real-world experiences
with uncertain reasoning systems' 12:00-12:45pm
Moderator: B. D'Ambrosio
Lunch 12:45-2:00pm
Plenary Session III: Representation and Independence
2:00-3:40pm
Context-Specific Independence in Bayesian Networks
C. Boutilier, N. Friedman, M. Goldszmidt, D. Koller
Binary Join Trees
P. Shenoy
Why is diagnosis using belief networks insensitive to imprecision in
probabilities?
M. Henrion, M. Pradhan, K. Huang, B. del Favero, G. Provan, P. O'Rorke
On separation criterion and recovery algorithm for chain graphs
M.n Studeny
UAI-KDD Special Joint Sessions
Portland Convention Center
Selected talks on learning graphical models from the UAI and KDD
proceedings. UAI badges will be honored at the Portland Convention
Center for the joint session.
Plenary Session X: Learning, Probability, and Graphical Models I
8:30-12:00pm
KDD: Knowledge Discovery and Data Mining: Toward a Unifying
Framework
U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth
UAI: Efficient Approximations for the Marginal Likelihood of
Incomplete Data Given a Bayesian Network
D. Chickering and D. Heckerman
KDD: Clustering using Monte Carlo Cross-Validation
P. Smyth
UAI: Learning Equivalence Classes of Bayesian Network Structures
D. Chickering
Break 9:45-10:05am
Plenary Session XI: Learning, Probability, and Graphical Models II
10:05-12:00pm
UAI: Learning Bayesian Networks with Local Structure
N. Friedman and M. Goldszmidt
KDD: Rethinking the Learning of Belief Network Probabilities
R. Musick
UAI: Bayesian Learning of Loglinear Models for Neural Connectivity
K. Laskey and L. Martignon
KDD: Harnessing Graphical Structure in Markov Chain Monte Carlo
Learning
P. Stolorz
General Conference Chair (General conference inquiries):
========================
Steve Hanks
Department of Computer Science and Engineering, FR-35
University of Washington
Seattle, WA 98195
Tel: (206) 543 4784
Fax: (206) 543 2969
Email: hanks@cs.washington.edu
UAI Program Committee
======================
Fahiem Bacchus, University of Waterloo, Cananda
Salem Benferhat, IRIT Universite Paul Sabatier, France
Philippe Besnard, IRISA, France
Mark Boddy, Honeywell Technology Center, USA
Piero Bonissone, General Electric Research Laboratory, USA
Craig Boutilier, University of British Columbia, Canada
Jack Breese, Microsoft Research, USA
Wray Buntine, Thinkbank, USA
Luis M. de Campos, Universidad de Granada, Spain
Enrique Castillo, Universidad de Cantabria, Spain
Eugene Charniak, Brown University, USA
Greg Cooper, University of Pittsburgh, USA
Bruce D'Ambrosio, Oregon State University, USA
Paul Dagum, Stanford University, USA
Adnan Darwiche, Rockwell Science Center, USA
Tom Dean, Brown University, USA
Denise Draper, University of Washington, USA
Marek Druzdzel, University of Pittsburgh, USA
Didier Dubois, IRIT Universite Paul Sabatier, France
Ward Edwards, University of Southern California, USA
Kazuo Ezawa, AT&T Labs, USA
Nir Friedman, Stanford University, USA
Robert Fung, Prevision, USA
Linda van der Gaag, Utrecht University, Netherlands
Hector Geffner, Universidad Simon Bolivar, Venezuela
Dan Geiger, Technion, Israel
Lluis Godo, Campus Universitat Autonoma Barcelona, Spain
Robert Goldman, Honeywell Technology Center, USA
Moises Goldszmidt, SRI International, USA
Adam Grove, NEC Research Institute, USA
Peter Haddawy, University of Wisconsin-Milwaukee, USA
Petr Hajek, Academy of Sciences, Czech Republic
Joseph Halpern, IBM Almaden Research Center, USA
Steve Hanks, University of Washington, USA
Othar Hansson, Thinkbank, USA
Peter Hart, Ricoh California Research Center, USA
David Heckerman, Microsoft Research, USA
Max Henrion, Lumina, USA
Frank Jensen, Hugin Expert A/S, Denmark
Michael Jordan, MIT, USA
Leslie Pack Kaelbling, Brown University, USA
Uffe Kjaerulff, Aalborg University, Denmark
Daphne Koller, Stanford University, USA
Paul Krause, Imperial Cancer Research Fund, UK
Rudolf Kruse, University of Braunschweig, Germany
Henry Kyburg, University of Rochester, USA
Jerome Lang, IRIT Universite Paul Sabatier, France
Kathryn Laskey, George Mason University, USA
Paul Lehner, George Mason University, USA
John Lemmer, Rome Laboratory, USA
Tod Levitt, IET, USA
Ramon Lopez de Mantaras, Spanish Scientific Research Council, Spain
David Madigan, University of Washington, USA
Christopher Meek, Carnegie Mellon University, USA
Serafin Moral, Universidad de Granada, Spain
Eric Neufeld, University of Saskatchewan, Canada
Ann Nicholson, Monash University, Australia
Ramesh Patil, Information Sciences Institute, USC, USA
Judea Pearl, University of California, Los Angeles, USA
Kim Leng Poh, National University of Singapore
David Poole, University of British Columbia, Canada
Henri Prade, IRIT Universite Paul Sabatier, France
Greg Provan, Institute for Learning Systems, USA
Enrique Ruspini, SRI International, USA
Romano Scozzafava, Dip. Me.Mo.Mat., Rome, Italy
Ross Shachter, Stanford University, USA
Prakash Shenoy, University of Kansas, USA
Philippe Smets, IRIDIA Universite libre de Bruxelles, Belgium
David Spiegelhalter, Cambridge University, UK
Peter Spirtes, Carnegie Mellon University, USA
Milan Studeny, Academy of Sciences, Czech Republic
Sampath Srinivas, Microsoft, USA
Jaap Suermondt, Hewlett Packard Laboratories, USA
Marco Valtorta, University of South Carolina, USA
Michael Wellman, University of Michigan, USA
Nic Wilson, Oxford Brookes University, UK
Yang Xiang, University of Regina, Canada
Hong Xu, IRIDIA Universite libre de Bruxelles, Belgium
John Yen, Texas A&M University, USA
Lian Wen Zhang, Hong Kong University of Science & Technology
Please return this form via email to hanks@cs.washington.edu
or use
the web-based registration form available at the UAI-96 home page at http://cuai-96.microsoft.com
to register online.
=================================
Registrant Information
=================================
Name: __________________________________
Affiliation: ____________________________
Address: ____________________________
____________________________
____________________________
Phone: ____________________________
Email address: ____________________________
=================================
Registration information
=================================
____ Register me for the conference
Non-student $275
Student $150
Students, please supply:
Advisor's name and Email address:
_______________________________________
____ Register me for the full-day course on
uncertain reasoning (July 31)
Non-student
with conference registration $85
without conference registration $135
Student
with conference registration $35
without conference registration $50
=================================
Dormitory accomodation
=================================
Singles, doubles, and triples are available.
All include a private bedroom; doubles and
triples share a bathroom.
Rates: Single $26.50 per night
Double $21.50 per night
Triple $16.50 per night
_____________ Arrival date (earliest July 30)
_____________ Departure date (latest August 4)
I am paying for _____ people
for _____ nights
at a daily rate of _________
for a total of _________
_______________________ Sharing with (doubles and triples only)
_______________________ Sharing with (triples only)
===============================
Meal service
===============================
Conference registration includes the conference banquet on
August 2nd.
Reed college offers a package of three lunches during the
conference for a total of $24.
____________ Please register me for the lunch service
=================================
Payment Summary
=================================
$______________ Conference registration
$______________ Full-day course registration
$______________ Lodging charges
$______________ Meal charges
$______________ TOTAL AMOUNT
__________ Please charge my ____ Visa
____ MasterCard
___________________ Card Number
______________ Expiration date
_________ I will send a check via surface mail.
Address for checks:
Steve Hanks
Department of Computer Science and Engineering
University of Washington
Box 352350
Seattle, WA 98195-2350
For questions about arrangements and registration issues, contact the
Steve Hanks (hanks@cs.washington.edu).
For questions about the program,
contact Eric Horvitz (horvitz@microsoft.com)
or Finn Jensen (fvj@iesd.auc.dk).
KDD Nuggets Index