Machine Learning List: Vol. 15, No. 6
Machine Learning List <[email protected]> Fri, 25 Apr 2003 00:02:50 -0700
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Machine Learning List: Vol. 15, No. 6
Thursday, April 24, 2003
Contents
Calls for Papers and Other Meeting Announcements
ECML/PKDD 2003 Discovery Challenge CfP
Call for Papers: 4th EKDB 03
cfp: AMR 2003 - 1st Intl. Wrkshp on Adaptive Multimedia Retrieval
CfP: KI-03 WS on Preference Learning
CFP: ICML Workshop - Continuum from Labeled to Unlabeled Data ...
CFP: Wrkshp on Probabilistic Graphical Models for Classification
CFP: Operational Text Classification 03 : Wash., DC 27-Aug-03
Announcement of Workshops/Tutorials of ECML/PKDD-2003
CFP: MRDM Wshp @ SIGKDD-2003
Career Opportunities
Position: Research Fellow in User Modeling
Program Director for AI & Cognitive Science at NSF
Misc. Other Announcements and Humor
KDD CUP 2003 Announcement
Announcement: Volume 1 of JMLG available.
The Machine Learning List is moderated. Contributions should be
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From: "Petr Berka" <[email protected]>
Subject: ECML/PKDD 2003 Discovery Challenge CfP
Date: Mon, 24 Mar 2003 13:20:34 +0100
ECML/PKDD 2003 Discovery Challenge
Call for Contributions
The Discovery Challenge will be held as a workshop at the 13th ECML +
6th PKDD conference, September 22-26, 2003, Cavtat-Dubrovnik, Croatia.
Data from medical domain are available to prospective participants for
download and analysis. The deadline for submissions is June, 30.
For more info about the Challenge, visit
http://lisp.vse.cz/challenge/ecmlpkdd2003
For more info about the ECML/PKDD2003, visit
http://www.cs.kuleuven.ac.be/conference/ecmlpkdd/
------------------------------
From: <[email protected]>
Subject: Call for Papers: 4th EKDB 03
Date: Mon, 24 Mar 2003 14:57:02 +0000
CALL FOR PAPERS
4th International Workshop on Extraction of Knowledge from Databases
(EKDB'03)
part of the 11th Portuguese Conference on Artificial
Intelligence (EPIA'03)
http://www.di.uevora.pt/epia03/
December 4-7, 2003
Beja, Portugal
Aims and Scope The objective of the workshop is to discuss methods for
non-trivial extraction of knowledge which is implicit in existing data
and which can be described in a high-level representation so as to
facilitate interpretation. Techniques from the machine learning,
statistics and database fields are highly relevant for this task.
Current real-world learning problems involve very large and complex
data sets. Although a large number of techniques has been developed
and applied, significant challenges remain, related with the design
and analysis of methologies to handle this type of problems.
The ability to incorporate new information and to react to concept
drift, are challenging topics for different learning communities. One
of the goals of this workshop is to promote an open discussion on
these and related topics between different communities that are
interested in these problems, namely the artificial intelligence,
control, statistics and database communities. EKDB-03 follows the
successful workshops EKDB-01, EKDB-99 and EKDB-97.
IMPORTANT DATES
May 18, 2003: Submission Deadline,
July 20, 2003: Author Notification,
September 13, 2003: Final versions due,
December 4-7, 2003: Workshop and Conference
------------------------------
From: "Marcin Detyniecki" <[email protected]>
Subject: cfp: AMR 2003 - 1st Intl. Wrkshp on Adaptive Multimedia Retrieval
Date: Thu, 27 Mar 2003 18:57:08 +0100
1st International Workshop on Adaptive Multimedia Retrieval
- AMR 2003 -
Part of KI 2003, 15-18 September 2003
University of Hamburg, Germany
(http://www.cs.berkeley.edu/~anuernb/amr2003/)
During the last years several approaches have been developed that
tackle specific problems of the retrieval process, e.g. feature
extraction methods for multimedia data, problem specific similarity
measures and interactive user interfaces. These methods enable the
design of efficient retrieval tools if the user is able to provide an
appropriate query. However, user specific interests and search context
are usually neglected when objects are retrieved.
To improve today's retrieval tools and thus the overall satisfaction of
a user, it is necessary to develop methods that are able to support the
user in the search process, e.g. by providing additional information
about the search results as well as the data collection itself and also
by adapting the retrieval tool to the user's needs and interests.
The goals of the workshop are to intensify the exchange of ideas
between different research communities to enable the design of improved
user adaptive retrieval tools. The workshop focuses especially on
researchers that are working on feature extraction techniques for
multimedia, computer linguistic approaches, (dynamic) data analysis
methods, and visualization methods as well as user interface design.
SUBMISSIONS:
Submissions should be formatted according to Springer LNCS style (see
http://www.springer.de/comp/lncs/authors.html). Papers should have
about 10 pages but should not exceed 15 pages and should be submitted
electronically in PDF or postscript.
IMPORTANT DATES:
May 24, 2003: Deadline for paper submission
June 10, 2003: Notification of acceptance
July 31, 2003: Deadline for final papers
VENUE:
The workshop will take place during the 26th German Conference on
Artificial Intelligence (KI 2003) in Hamburg, Germany. Information
about the venue, hotels, etc. are provided on the Web pages of the main
conference KI 2003 (http://www.ki2003.de/).
Further details can be found on the Web page of the workshop:
http://www.cs.berkeley.edu/~anuernb/amr2003/
------------------------------
From: [email protected] (Johannes Fuernkranz)
Subject: CfP: KI-03 WS on Preference Learning
Date: Thu, 10 Apr 2003 09:56:32 +0200
Preference Learning: Models, Methods, Applications
http://www.mathematik.uni-marburg.de/~eyke/Research/KI03WS.html
A Workshop to be held as part of the conference KI-2003
<http://www.ki2003.de/>, September 15-18, 2003, Hamburg
WORKSHOP CONTENTS
The focus of the workshop will be on machine learning methods for
preference elicitation, i.e. on methods for inducing preferences from
given observations. Like other types of complex learning tasks that
have recently entered the stage in the field of machine learning,
preference learning deviates strongly from the standard machine
learning problems of classification and regression. It is particularly
challenging because it involves the prediction of complex structures,
such as weak or partial order relations, rather than single
values. Moreover, training input will not, as it is usually the case,
be offered in the form of complete examples but may comprise more
general types of information, such as relative preferences or
different kinds of indirect feedback. For example, learning problems
might be posed by providing - or, in the style of an active learner,
by asking for - preference relations between the training examples
rather than a target value (as in supervised learning) or a utility
degree (as in reinforcement learning).
WORKSHOP GOALS
The workshop pursues two main goals. Firstly, to discuss recent
advances in preference elicitation through machine learning. Secondly,
to stimulate new research avenues in this evolving field, by providing
a discussion forum for both researchers in machine learning and
potential users of preference elicitation techniques in all areas of
Artificial Intelligence.
Topics of interest include, but are not limited to
* machine learning methods for preference elicitation,
* extensions of the common frameworks for machine learning
(supervised, unsupervised and reinforcement learning),
* quantitative and qualitative approaches to preference modeling,
* formal modeling of training examples and different forms of feedback,
* applications of preference elicitation in various fields, e.g., in
electronic commerce, personalization, or collaborative filtering.
As the workshop is intended to support an exchange of ideas between
different research areas interested in preference elicitation, it
should appeal to both, researchers that work on preference elicitation
techniques and tools (primarily people working in machine learning and
related fields such as data mining, knowledge discovery, and
statistics), as well as participants from other fields interested in
preference elicitation (such as decision and game theory,
autonomous/software/web agents, information retrieval, knowledge
representation, negotiation, personalization, user modeling, or web
intelligence).
SCHEDULE
Submission of extended abstracts: May 31, 2003
Acceptance notification: June 15, 2003
Final manuscripts: July 31, 2003
FURTHER INFORMATION
Consult the Workshop home-page at
http://www.mathematik.uni-marburg.de/~eyke/Research/KI03WS.html
------------------------------
From: [email protected]
Subject: CFP: ICML Workshop - Continuum from Labeled to Unlabeled Data ...
Date: Mon, 14 Apr 2003 13:28:25 -0500
CALL FOR PAPERS
ICML 2003 Workshop (Co-located with KDD 2003)
The Continuum from Labeled to Unlabeled Data in Machine Learning and Data
Mining
(Special emphasis on real-world applications and problems)
August 21, 2003. Washington, DC.
http://www.accenture.com/techlabs/icmlworkshop2003/
PAPERS DUE: May 1, 2003
WORKSHOP DESCRIPTION:
There is a spectrum of ways to use data in machine learning and data
mining. At the one end is completely unsupervised learning or
clustering, and at the other end is supervised learning where the
target output is known for every instance.
This workshop aims to explore the space between these extremes, with
particular attention to a variety of real-world applications. Papers
addressing novel types of data, methods of diagnosing when unlabeled
data will help and when it will hinder, and applying techniques across
multiple application domains and multiple levels of supervision are
particularly encouraged. Papers discussing the acquisition of labels
from real-world experts in real-world data mining problems are also
encouraged. Data mining practitioners working on real-world problems
with large amounts of captured/stored data but a high cost labeling
process are encouraged to submit problem descriptions and possible
solutions.
FOR MORE DETAILS, see http://www.accenture.com/techlabs/icmlworkshop2003
------------------------------
From: <[email protected]>
Subject: CFP: Wrkshp on Probabilistic Graphical Models for Classification
Date: Tue, 15 Apr 2003 12:33:29 +0200 (MET DST)
WORKSHOP
PROBABILISTIC GRAPHICAL MODELS FOR CLASSIFICATION
during the
14th European Conference on Machine Learning (ECML) and
the 7th European Conference on Principles and Practice of
Knowledge Discovery in Databases (PKDD)
September 23, 2003, Cavtat-Dubrovnik, Croatia
Workshop web page:
http://www.sc.ehu.es/ccwbayes/ecml-pkdd-03-workshop/call.htm
SCHEDULE
+ Paper submission deadline - 13 June, 2003
+ Notification to authors - 4 July, 2003
+ Camera-ready papers - 11 July, 2003
+ Workshop date - 23 September, 2003
SCOPE
Probabilistic graphical model paradigm has become a popular tool for
encoding, representing and handling uncertain knowledge in expert
systems over the last decade. Applications of this paradigm include
wide areas of the reality (medicine, agriculture, economy,
bioinformatics...). Currently, interest is emerging within
probabilistic graphical models to use them as a tool to induce
supervised-unsupervised classification models. Taking the well-known
naive-Bayes classifier as a basic, extensions and improvements of this
simple but effective algorithm are being proposed from the field of
probabilistic graphical models in the last ten years. The works of
prestigious authors of the area of machine learning, have enhanced the
role of probabilistic graphical models to solve classification tasks.
Apart from the desired high accuracy of the model, these approaches
offer the opportunity to graphically show the probabilistic
relationships between domain attributes. Among these relevant
approaches the selective Bayesian classifier, the Autoclass procedure,
the tree-augmented network, or exact model averaging with naive Bayes
can be cited. These works, coupled with the spectacular development of
the Bayesian network paradigm, are opening a wide range of
possibilities to adapt probabilistic graphical models to solve
classification tasks.
Contributed works in this workshop should ideally be in the area of
new algorithmic, theoretical approaches and applications in the use of
probabilistic graphical models to solve supervised and unsupervised
classification tasks.
------------------------------
From: "Dave L" <[email protected]>
Subject: CFP: Operational Text Classification 03 : Wash., DC 27-Aug-03
Date: Thu, 17 Apr 2003 22:52:40 -0500
CALL FOR PARTICIPATION
Third Workshop on Operational Text Classification (OTC-03)
August 27, 2003
Washington, DC (co-located with KDD 2003 Conference)
The OTC workshops feature talks and discussion by developers and users
of text classification in a range of real-world settings. The 2003
workshop particularly encourages presentations on uses of text
classification in text & data mining. However, ALL applications of
text classification are of interest, including controlled vocabulary
and web directory indexing, construction of specialized information
feeds, information security, help desk automation, content filtering
(e.g. spam, pornography), and alerting.
Prospective speakers should submit an abstract (maximum 750 words) to
[email protected] by June 8, 2003. Visit
http://www.daviddlewis.com/events/otc2003 for more information, or
write [email protected].
------------------------------
From: Luis Torgo <[email protected]>
Subject: Announcement of Workshops/Tutorials of ECML/PKDD-2003
Date: Tue, 22 Apr 2003 14:26:58 +0000
14th European Conf. on Machine Learning (ECML-03)
and
7th European Conf. on Principles and Practice of
Knowledge Discovery in Databases (PKDD-03)
22-26 September 2003, Cavtat-Dubrovnik, Croatia
http://www.cs.kuleuven.ac.be/conference/ecmlpkdd/
WORKSHOPS
W1: First European Web Mining Forum
http://km.aifb.uni-karlsruhe.de/ws/ewmf03/
W2: Multimedia Discovery and Mining
http://ai.ijs.si/Dunja/MultimediaMining03/
W3: Data Mining and Text Mining in Bioinformatics
http://kd.cs.uni-magdeburg.de/ws03.html
W4: Knowledge Discovery in Inductive Databases
http://www.cinq-project.org/ecmlpkdd2003/
W5: Graph, Tree and Sequence Mining
http://www.ar.sanken.osaka-u.ac.jp/MGTS-2003CFP.html
W6: Probabilistic Graphical Models for Classification
http://www.sc.ehu.es/ccwbayes/ecml-pkdd-03-workshop/call.htm
W7: Parallel and Distributed Computing for Machine Learning
http://www.fe.up.pt/~rcamacho/ECML03-W7.html
TUTORIALS
T1: KD Standards
http://www.comp.rgu.ac.uk/staff/dw/kd_standards.html
T2: Data Mining and Machine Learning in Time Series Databases
http://www.cs.ucr.edu/~eamonn/ECML_PKDD_03.html
T3: Exploratory Analysis of Spatial Data and Decision Making using Interactive
Maps and Linked Dynamic Displays
http://www.commongis.com/tutorial/tutorial-PKDD-2003.html
T4: Music Data Mining
http://www.soi.city.ac.uk/~geraint/conklin/
TUTORIAL/WORKSHOP COMBOS
T/W1: Context-Free Grammar Learning
http://ilk.uvt.nl/~mvzaanen/ECMLPKDD/index.html
T/W2: Adaptive Text Extraction and Mining
http://www.dcs.shef.ac.uk/~fabio/ATEM03/
CHALLENGE WORKSHOP
ECML/PKDD2003 Discovery Challenge: A Collaborative Effort in Knowledge
Discovery from Databases
http://lisp.vse.cz/challenge/ecmlpkdd2003/chall2003.htm
------------------------------
From: Saso Dzeroski <[email protected]>
Subject: CFP: MRDM Wshp @ SIGKDD-2003
Date: Thu, 24 Apr 2003 12:52:07 +0200
CALL FOR PAPERS
MRDM 2003 - 2nd Workshop on Multi-Relational Data Mining
organised at the
9th ACM SIGKDD International Conference
on Knowledge Discovery & Data Mining
August 24 - 27, 2003, Washington DC, USA
PAPER SUBMISSIONS DUE: 6 June 2003
WORKSHOP WEBSITE: http://www-ai.ijs.si/SasoDzeroski/MRDM2003/
WORKSHOP DATE: 27 August 2003
Multi-Relational Data Mining (MRDM) is the multi-disciplinary field
dealing with knowledge discovery from relational databases consisting
of multiple tables. Mining data which consists of complex/structured
objects also falls within the scope of this field, since the
normalized representation of such objects in a relational database
requires multiple tables. The field aims at integrating results from
existing fields such as inductive logic programming, KDD, machine
learning and relational databases; producing new techniques for mining
multi-relational data; and practical applications of such tecniques.
The aim of the workshop is to bring together researchers and
practitioners of data mining interested in methods for finding
patterns in expressive languages from
complex/multi-relational/structured data and their applications.
TOPICS OF INTEREST
The topics of interest (listed in alphabetical order) include,
but are not limited to, the following:
- Applications of (multi-)relational data mining
- Data mining problems that require (multi-)relational methods
- Distance-based methods for structured/relational data
- Inductive databases
- Kernel methods for structured/relational data
- Learning in probabilistic relational representations
- Link analysis and discovery
- Methods for (multi-)relational data mining
- Mining structured data, such as amino-acid sequences,
chemical compounds, HTML and XML documents, ...
- Propositionalization methods for transforming (multi-)relational
data mining problems to single-table data mining problems
- Relational neural networks
- Relational pattern languages
We also encourage submissions which present early stages
of research work, software, and applications.
------------------------------
From: "Geoff Webb" <[email protected]>
Subject: Position: Research Fellow in User Modeling
Date: Tue, 25 Mar 2003 15:42:24 +1100
Research Fellow in Computer Science
School of Computer Science & Software Engineering
a.. Department/Faculty: School of Computer Science & Engineering
b.. Location: Clayton campus
c.. Closing Date: 09/04/03
Applications are invited from qualified people for an appointment of
Research Fellow for eighteen months in the area of user-modeling of
web-site users. The applicant should have a PhD in Computer Science or a
related field.
The applicant should be familiar with user-modeling, data-mining, or
machine-learning techniques, and should have good programming skills.
Programming in C++, C, or Java is an advantage.
The Benefits:
$54,864 - $65,152 p.a. Level B
Location:
Clayton campus
Contact:
Professor Geoff Webb, Tel. 9905 3296 or email
[email protected] for inquiries and information.
Applications:
Professor G Webb, School of Computer Science & Software Engineering,
Monash University, Vic 3800 or email as above by 9/04/2003. Quote Ref
No. A034226 and include curriculum vitae and the names (with phone and
facsimile numbers) of three referees in your application.
------------------------------
From: "Pazzani, Michael J." <[email protected]>
Subject: Program Director for AI & Cognitive Science at NSF
Date: Tue, 15 Apr 2003 10:54:02 -0400
The Information and Intelligent Systems Division of CISE at NSF is
recruiting a program director for the Artificial Intelligence &
Cognitive Science Program. The name of this program was recently
changed from Knowledge and Cognitive Systems to more accurately
reflect the research it supports. The program covers areas of AI &
Cognitive Science including Planning, Knowledge Representation,
Machine Learning, Automated Reasoning, Integrated Agents and models of
cognitive processes.
The program director may be hired as a NSF employee or as a "rotator"
from a university or government position. One mechanism to hire a
"rotator" is to make a grant to your home institution that pays your
12-month salary, with NSF providing additional funds for housing and
travel back to your home institution. Applications are due May 15,
2003. See http://www.cise.nsf.gov/vacn/index.html for more details
and the application process.
The Artificial Intelligence & Cognitive Science Program is one of
several related programs in the division of Information and
Intelligent Systems, which also includes programs in Robotics and
Computer Vision, Human Language and Communication, Human Computer
Interaction, Information and Data Management, Digital Libraries,
Digital Society and Technologies, Universal Access, and Data and
Application Security.
Qualified persons who are women, ethnic/racial minorities, and persons
with disabilities are strongly encouraged to apply. The National
Science Foundation is an Equal Opportunity Employer committed to
employing a highly qualified staff that reflects the diversity of our
nation.
If you'd like more information or would like to recommend someone to
me, please contact me at the address below.
Michael J. Pazzani
Division Director, Information and Intelligent Systems
National Science Foundation
4201 Wilson Boulevard, Suite 1115,
Arlington, VA 22230
Bus: 703-292-8930
Bus Fax: 703-292-9073
E-mail: [email protected]
http://www.cise.nsf.gov/iis
------------------------------
From: Osmar Zaiane <[email protected]>
Subject: KDD CUP 2003 Announcement
Date: Mon, 7 Apr 2003 16:36:34 -0600 (MDT)
KDD Cup 2003 (http://www.cs.cornell.edu/projects/kddcup/index.html)
The Ninth ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining (KDD 2003)
Every year, in conjunction with the ACM SIGKDD conference, a knowledge
discovery and data mining competition (KDD Cup) is held to challenge
the research community in industry and academia.
This year's competition focuses on problems motivated by network
mining and the analysis of usage logs. Complex networks have emerged
as a central theme in data mining applications, appearing in domains
that range from communication networks and the Web, to biological
interaction networks, to social networks and homeland security.
At the same time, the difficulty in obtaining complete and accurate
representations of large networks has been an obstacle to research in
this area.
This KDD Cup is based on a very large archive of research papers that
provides an unusually comprehensive snapshot of a particular social
network in action; in addition to the full text of research papers, it
includes both explicit citation structure and (partial) data on the
downloading of papers by users. It provides a framework for testing
general network and usage mining techniques, which will be explored
via four varied and interesting task. Each task is a separate
competition with its own specific goals.
To learn more about the KDD cup competition rules, the tasks, and the
datasets, please visit the KDD cup web site managed by
the KDD cup chairs Johannes Gehrke, Paul Ginsparg and Jon Kleinberg.
For more information, please refer to the SIGKDD Conference web site
http://www.acm.org/sigkdd/kdd2003/
or go directly to the KDD Cup 2003 web site
http://www.cs.cornell.edu/projects/kddcup/index.html
------------------------------
From: "Cycle L. Bittmap, Ph.D." <[email protected]>
Subject: Announcement: Volume 1 of JMLG available.
Date: Tue, 01 Apr 2003 05:30:03 -0800
To the AI and ML community:
We are pleased to announce the first volume of a new online journal,
the Journal of Machine Learning Gossip,
(http://www.jmlg.org/papers.htm). The volume includes the following
award-winning papers:
Markov Indecision Processes: A Formal Model of Decision-Making Under
Extreme Confusion
by Harry Q. Bovik, Judy Q. Goldsmith, Andrew Q. Klapper,
and Michael Q. Littman
Data Set Selection
by Doudou LaLoudouana and Mambobo Bonouliqui Tarare
On the Origin and Destiny of Inductive Machine Learning
by Terran Lane
Visit our web site jmlg.org/papers.htm for access to these papers and
for more information about the journal and its goals.
We look forward to serving you in the coming years.
Sincerely,
Cycle L. Bittmap, PhD
on behalf of the editors of the JMLG
------------------------------
End of ML-LIST Digest Vol 15, No. 6
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