Machine Learning List: Vol. 14, No. 8
Saturday, Nov. 2, 2002
Contents
Calls for Papers and Other Announcements
Call for papers
Context03. Notice: dates have changed!!!
CFP: ICML-2003 (Twentieth International Conference on Machine Learning)
Second CFP: 9th International Conference on User Modeling (UM 2003)
CFP -- MLJ special issue on Data Mining Lessons Learned
IEEE Transactions on SMCB Special Issue on Distributed and Mobile Data Mining
Call for contributions - Anticipatory Behavior in Adaptive Learning Systems
to include in the ML list
GPEM journal special issue on Biological apps of Evolutionary Computation
Jobs and Research Opportunities
Postdoctoral Fellowships: Expressions of Interest
contribution to MLlist
New MSc Intelligent Systems
Alberta Ingenuity Centre for Machine Learning
Other Items of Interest
ANN: RocOn - ROC Visualisation Tool
The Machine Learning List is moderated. Contributions should be
relevant to the scientific study of machine learning. Please send
submissions for distribution to: [EMAIL PROTECTED] For requests to be
added, removed, or to change your email address, send email to:
[EMAIL PROTECTED]
In general, submissions should be no more than a few full-screens of
text. For meeting announcements, highlight the conference or workshop
web page and give a summary description of the goals of the event.
Information such as the list of program committee members, talk
schedules, and registration forms are unnecessary and should not be
included. Job adds are usually no more than a few full-screens so
they should fit naturally.
----------------------------------------------------------------------
From: Sanja Petrovic <[EMAIL PROTECTED]>
Subject: Call for papers
Date: Fri, 04 Oct 2002 17:16:04 +0100
CALL FOR PAPERS:
Special Issue of JOURNAL OF SCHEDULING on
EXPERT SYSTEMS AND MACHINE LEARNING IN SCHEDULING
Guest Editor: Sanja Petrovic
In recent years there has been an increased interest in the
application of expert system methodology to solving complex planning
and scheduling problems. This technology provides an appropriate way
to build systems that can make use of the knowledge and experience of
scheduling experts.
A number of promising research areas have become apparent. Particular
examples include scheduling systems which are able to learn and adapt
to new situations, systems which can handle uncertain knowledge and
incomplete information, etc.
A special issue of the Journal of Scheduling will be devoted to expert
systems and machine learning technology across a variety of scheduling
and scheduling-related problems and domains.
Topics covered in the special issue may include, but are not restricted
to, machine learning and expert system approaches to:
- dynamic scheduling environments
- repair problems
- evaluation of schedules
- planning and scheduling of large size problems
- distributed planning and scheduling
Potential papers could cover a variety of expert systems/machine
learning research areas including:
- case based reasoning
- neural networks
- fuzzy logic
- artificial immune systems
- constraint-based scheduling
DATES AND INFORMATION:
Deadline for submissions: December 1, 2002
Notification of decision: July 1, 2003
Final versions due: December 1, 2003
Special issue will appear: 2004
Detailed instructions for authors can be found on the Notes for
Contributors page of any issues of the journal or on the Web page on
"Journal of Scheduling":
http://www.interscience.wiley.com/jpages/1094-6136/
------------------------------
From: "Roberta Ferrario" <[EMAIL PROTECTED]>
Subject: Context03. Notice: dates have changed!!!
Date: Wed, 9 Oct 2002 10:30:24 +0200
PLEASE NOTICE THAT BOTH THE DATES OF THE CONFERENCE AND THE
DEADLINE FOR SUBMISSIONS HAVE BEEN POSTP0NED!
| CONTEXT'03 |
| |
| Fourth International and Interdisciplinary Conference on |
| Modeling and Using Context |
| |
| Stanford, California (USA) |
| June 23-25, 2003 |
| |
| (www.context.umcs.maine.edu/CONTEXT-03) |
The Fourth International and Interdisciplinary Conference on Modeling
and Using Context (CONTEXT'03) will provide a high-quality forum for
discussions about context among researchers active in artificial
intelligence and other areas of computer science, cognitive science,
linguistics, the organizational sciences, philosophy, and psychology.
Context affects a wide range of activities in humans and animals as
well as in artificial agents and other computer programs. The
importance of context is widely acknowledged, and "context" has become
an area of study in its own right, as evidenced by the numerous
workshops, symposia, seminars, and conferences held recently. CONTEXT,
the oldest conference series focusing on context, is unique due to its
strong emphasis on interdisciplinary research. Previous CONTEXT
conferences have been held in Rio de Janeiro, Brazil (CONTEXT'97),
Trento, Italy (CONTEXT'99), and Dundee, Scotland (CONTEXT'01). Each of
these brought together researchers in many disparate fields to discuss
and report on research on context-related topics.
SUBMISSION OF PAPERS
Since CONTEXT'03 will be an interdisciplinary forum, all submissions,
in addition to being evaluated for their technical merit, will be
evaluated for their accessibility to an interdisciplinary audience.
Works that transcend disciplinary boundaries are especially
encouraged. Papers will be accepted either for oral presentation or
for presentation at a poster session.
Each submission will be evaluated by three referees. Complete
formatting requirements and detailed instructions for authors can be
found on the conference Web page. Note that papers cannot be longer
than 14 pages. Papers must be submitted electronically--no hardcopy
submissions will be accepted without prior approval from the Program
Co-Chairs well in advance of the submission deadline. LaTeX and Word
templates are available at the conference Web page. Papers must be in
PDF format. See the conference Web page for instructions on
converting to this format from Word, LaTeX, etc.
IMPORTANT DATES
Paper submission deadline...........................January 27, 2003
Notification of acceptance/rejection .................March 13, 2003
Deadline for final versions of accepted papers........April 13, 2003
Conference..........................................June 23-25, 2003
For more information, see http://www.context.umcs.maine.edu
------------------------------
From: Tom Fawcett <[EMAIL PROTECTED]>
Subject: CFP: ICML-2003 (Twentieth International Conference on Machine Learning)
Date: Thu, 10 Oct 2002 18:35:38 -0700
Call for Papers
The Twentieth International Conference on Machine Learning
Washington, DC USA
August 21-24, 2003
The Twentieth International Conference on Machine Learning (ICML-2003) will
be held in Washington D.C. August 21-24, 2003. The conference will bring
together researchers to exchange ideas and report recent progress in the
field of machine learning.
TOPICS FOR SUBMISSION
ICML-2003 welcomes submissions on all topics related to machine learning.
In addition to the topics that traditionally are represented at machine
learning conferences, we specifically encourage papers on the following
topics:
- Applications of machine learning, particularly:
1. exploratory research that describes novel learning tasks;
2. applications that require non-standard techniques or shed light on
limitations of existing learning techniques; and
3. work that investigates the effect of the developers' decisions about
problem formulation, representation or data quality on the learning
process.
- Analysis of learning algorithms that demonstrate generalization
ability and also lead to better understanding of the computational
complexity of learning.
- The role of learning in spatial reasoning, motor control, and more
generally in the performance of intelligent autonomous agents.
- The discovery of scientific laws and taxonomies, and the induction
of structured models from data.
- Computational models of human learning.
- Novel formulations of and insights into data clustering.
- Learning from non-static data sources: incremental induction,
on-line learning and learning from data streams.
Submissions that demonstrate both theoretical and empirical rigor are
especially encouraged.
FORMAT OF THE CONFERENCE
The conference will include one day of workshops and tutorials and three
days of technical presentations, poster sessions and informal gatherings
designed to foster discussion of research in machine learning.
The conference will include both plenary and parallel tracks for the
presentation of papers published in the conference proceedings. Speakers
will also present their work at an evening poster session, which will allow
conference attendees to discuss the work with the authors at greater
length. In addition to presentations of refereed papers, the conference
will include talks by several invited speakers.
The conference will be co-located with KDD-2003 and COLT-2003. Details of
the co-location will be announced shortly.
IMPORTANT DATES
Abstracts due: February 10, 2003
Submissions due: February 14, 2003
Acceptance decisions mailed to authors: April 25, 2003
Camera-ready copies of all accepted papers due: May 16, 2003
Authors of conditionally accepted
papers notified: May 23, 2003
ADDITIONAL INFORMATION
For additional information, please see the conference web site:
http://www.hpl.hp.com/conferences/icml2003
which will provide additional details as they become available.
------------------------------
From: Ayse S Goker <[EMAIL PROTECTED]>
Subject: Second CFP: 9th International Conference on User Modeling (UM 2003)
Date: Sun, 13 Oct 2002 18:48:57 +0100 (BST)
UM 2003: 9th International Conference on User Modeling
http://www2.sis.pitt.edu/~um2003/
June 22 to June 26, 2003
University of Pittsburgh Conference Center
Johnstown, Pennsylvania, USA
CALL FOR PAPERS
The International User Modeling Conferences are the events at which
research foundations are being laid for the personalization of
computer systems. In the last 15 years, the field of User Modelling
has produced significant new theories and methods to analyze and model
computer users in short and long-term interactions. A user model is an
explicit representation of properties of individual users or user
classes. It allows the system to adapt its performance to user needs
and preferences. Methods for personalizing human-computer interaction
based on user models have been successfully developed, applied and
evaluated in a number of domains, such as information filtering,
e-commerce, adaptive natural language and hypermedia presentation and
tutoring systems.
New trends in HCI create new and interesting challenges for User
Modeling. While consolidating results in traditional domains of
interest, the User Modeling field now also addresses problems of
personalized interaction in mobile, ubiquitous and context-aware
computing and in user interactions with embodied, autonomous agents.
It also considers adaptation to user attitudes and affective stat
Previous successes in User Modeling research reflect the cooperation
of researchers in different fields, including artificial intelligence,
human-computer interaction, education, cognitive psychology and
linguistics. The International User Modeling Conferences are
characterized by active participation of people from these areas and
by lively discussions in a pleasant environment. UM 2003 is the latest
in a conference series begun in 1986, and follows recent meetings in
Sonthofen (2001), Banff (1999), Sardinia (1997), Hawaii (1996) and
Cape Cod (1994). As in past conferences, UM03 offers the following
forms of participation: tutorials, invited talks, paper and poster
sessions, a doctoral consortium, workshops and system demonstratio
DEADLINES
November 11, 2002 - preliminary workshop proposals
November 18, 2002 - papers
November 25, 2002 - posters
November 25, 2002 - final workshop proposals
November 25, 2002 - tutorial proposals
January 25, 2003 - Doctoral Consortium submissions
------------------------------
From: Tom Fawcett <[EMAIL PROTECTED]>
Subject: CFP -- MLJ special issue on Data Mining Lessons Learned
Date: Thu, 17 Oct 2002 14:21:24 -0700
Call for Papers
Machine Learning journal Special Issue
on Data Mining Lessons Learned
Guest editors:
Nada Lavrac, Hiroshi Motoda and Tom Fawcett
DESCRIPTION:
Data mining is concerned with finding interesting or valuable patterns
in data. Many techniques have emerged for analyzing and visualizing
large volumes of data, and what we see in the technical literature are
mostly success stories of these techniques. We rarely hear of steps
leading to success, failed attempts, or critical representation choices
made; and rarely do papers include expert evaluations of achieved
results. Insightful analyses of successful and unsuccessful
applications are crucial for increasing our understanding of machine
learning techniques and their limitations.
Challenge problems (such as the KDD Cup, COIL and PTE challenges) have
become popular in recent years and have attracted numerous
participants. These challenge problems usually involve a single
difficult problem domain, and participants are evaluated by how well
their entries satisfy a domain expert. The results of such challenges
can be a useful source of feedback to the research community.
At ICML-2002 a workshop on Data Mining Lessons Learned was held and was
well attended. This special issue of the Machine Learning journal
follows the main goals of that workshop, which are to gather experience
from successful and unsuccessful data mining endeavors, and to extract
the lessons learned from them.
GOALS:
The aim of this special issue is to collect the experience gained from
data mining applications and challenge competitions. We are interested
in lessons learned both from successes and from failures. Authors are
invited to report on experiences with challenge problems, experiences in
engineering representations for practical problems, and in interacting
with experts evaluating solutions. We are also interested in why some
particular solutions -- despite good performance -- were not used in
practice, or required additional treatment before they could be used.
SUBMISSION INSTRUCTIONS
Manuscripts for submission should be prepared according to the
instructions at http://www.cs.ualberta.ca/~holte/mlj/
In preparing submissions, authors should follow the standard
instructions for the Machine Learning journal at
http://www.cs.ualberta.ca/~holte/mlj/initialsubmission.pdf
Submissions should be sent via email to Hiroshi Motoda
([EMAIL PROTECTED]), as well as to Kluwer Academic
Publishers ([EMAIL PROTECTED]). In the email please state very
clearly that the submission is for the special issue on Data
Mining Lessons Learned.
DEADLINE
submissions: Monday, 7 April, 2003.
------------------------------
From: Hoony Park <[EMAIL PROTECTED]>
Subject: IEEE Transactions on SMCB Special Issue on Distributed and Mobile Data Mining
Date: Wed, 23 Oct 2002 12:46:15 -0400
Call for Papers
SPECIAL ISSUE ON DISTRIBUTED AND MOBILE DATA MINING
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, PART B
GUEST EDITORS:
Hillol Kargupta, Sanghamitra Bandyopadhyay, Byung-Hoon Park
SCOPE:
Knowledge discovery and data mining deal with the problem of
extracting interesting associations, classifiers, clusters, and other
patterns from data. The emergence of network-based computing
environments has introduced a new and important dimension to this
problem, viz., that of distributed sources of data and computing. The
Internet, corporate intranets, sensor networks, and even scientific
computing domains (e.g., distributed active archive centers (DAAC) of
the NASA Earth Observing System) support this observation. The advent
of laptops, palmtops, handhelds, embedded systems, and wearable
computers is also making ubiquitous access to a large quantity of
distributed data a reality. Advanced analysis of distributed data for
extracting useful knowledge is the next natural step in the
increasingly connected world of ubiquitous and distributed computing.
Most of the popular data mining algorithms are designed to work for
centralized data and they often do not pay attention to the resource
constraints of distributed and mobile environments. Recent research in
this area has demonstrated that handling these resource constraints in
an optimal fashion requires a new breed of data mining algorithms and
systems that are very different from their centralized
counterparts. This special issue will focus on the state-of-the-art
developments in the domain of distributed and mobile data mining.
PAPER SUBMISSION INSTRUCTION:
Your paper must be submitted in Portable Document Format (pdf) or
Postscript. It must print correctly on 8.5 X 11 inch paper. For
Unix and Windows systems there are postscript to pdf converters,
notably ps2pdf which is a part of ghostscript. A text version of
your abstract is required. When you are ready to submit, please
follow this link to the SMC ManuscriptCentral site:
http://smcb-ieee.manuscriptcentral.com/
You will have to create an account, if you do not yet have one.
Then you will log in and be asked for contact information, keywords
and an abstract. You will then upload your paper and any attachment
files (see for more information http://isl.csee.usf.edu/smcB). In
the notes to the editor, please clearly indicate that this paper is
for our special issue so that it gets routed correctly. Then a paper
number will be generated and returned to you. The site has
instructions and/or help buttons on each page. After the submission
please send a note to [EMAIL PROTECTED] with the title of the paper
and the names of the authors.
TARGET DATES:
Submission deadline: January 1, 2003
Acceptance notification: April 2, 2003
Final Papers: May 30, 2003
------------------------------
From: Martin Butz <[EMAIL PROTECTED]>
Subject: Call for contributions - Anticipatory Behavior in Adaptive Learning Systems
Date: Thu, 31 Oct 2002 13:38:34 -0600 (CST)
ABiALS 2002
Post Proceedings Book:
"Anticipatory Behavior in Adaptive Learning Systems:
Foundations, Theories, and Systems"
This upcoming volume addresses the question of when, where, and how
anticipations are useful in adaptive systems. Anticipations refer to the
influence of future predictions or future expectations on behavior and
learning.
ABiALS 2002 was a first interdisciplinary gathering of people interested
in how anticipations can be used efficiently to improve behavior and
learning. Four fundamentally different systems were distinguished:
(1) Implicitly anticipatory systems are those that act/learn in an
intelligent way but do not include any predictive bias in the applied
learning and/or behavioral mechanisms.
(2) Payoff anticipatory systems are those systems that do compare payoff
predictions for action decisions but do not use any state predictions.
(3) Sensory anticipatory systems are systems that use sensory predictions
to improve perceptual processing (e.g. preparatory attention).
(4) State anticipatory systems are systems that form explicit future
predictions/expectations that influence action decisions and learning.
The book "Anticipatory Behavior in Adaptive Learning Systems" will address
the latter two of the four types of systems. Submissions are welcome that
are concerned with any type of sensory anticipatory or state anticipatory
system.
AIM AND OBJECTIVES:
Most of the research over the last years in artificial adaptive behavior
concerned with model learning and anticipatory behavior has focused on the
model learning side. Research is particularly engaged in online
generalized model learning. Until now, though, exploitation of the model
has been done mainly to show that exploitation is possible or that an
appropriate model exists in the first place. Only very few applications
are available that show the utility of the model for the simulation of
anticipatory behavior.
The aim of this book is to lay out the foundations for a study of
anticipatory learning and behavior. The content will be divided roughly
into three chapters. The first chapter will provide psychological
background that not only supports the presence of anticipatory mechanisms
in ``higher'' animals and humans but also sheds light on when and why
anticipatory mechanisms can be useful. Chapter 2 will provide foundations
and frameworks for the study of anticipatory mechanisms distinguishing
fundamentally different mechanisms. Finally, Chapter 3 will contain
examples of implemented frameworks and systems.
Submission deadline is DECEMBER 20, 2002.
For more information please refer to the workshop page:
http://www-illigal.ge.uiuc.edu/ABiALS/
Please also see our introductory talk to the workshop for more detailed
information on anticipations and different types of anticipatory behavior:
http://www-illigal.ge.uiuc.edu/ABiALS/ABiALS2002Introduction.htm
There is also an introductory paper available that provides further
general information on the topic:
http://www-illigal.ge.uiuc.edu/ABiALS/Papers/ABiALS2002Intro.pdf
IMPORTANT DATES:
20.December 2002: Deadline for Submissions
24.January 2002: Notification of Acceptance
21.February 2002: Camera Ready Version for LNAI Volume
------------------------------
From: Stan Matwin <[EMAIL PROTECTED]>
Subject: to include in the ML list
Date: Thu, 31 Oct 2002 15:33:16 +0000
Machine Learning Journal
Special Issue on Inductive Logic Programming and Relational Learning
Following the 13th Int'l Conference on ILP in Sydney, Australia, in
Julky 2002, ML Journal has invited a Special Issue on ILP and relational
Learning. Please see the CFP at
http://www.site.uottawa.ca/~stan/MLJ-SI-ILP/cfp1.htm
------------------------------
From: [EMAIL PROTECTED] (James A. Foster)
Subject: GPEM journal special issue on Biological applications of Evolutionary
Computation
Date: Fri, 1 Nov 2002 11:52:18 -0800
Journal of
GENETIC PROGRAMMING AND EVOLVABLE MACHINES
Submissions are invited for a special issue of the journal on the theme of
BIOLOGICAL APPLICATIONS OF GENETIC AND EVOLUTIONARY COMPUTATION
Wolfgang Banzhaf and James Foster, editors
The field of Genetic and Evolutionary Computation has greatly benefited
by borrowing ideas from Biology. Recently, it has become clear that GEC
can help to solve biological problems, and thereby to "repay the debt".
It is also becoming apparent that the computer itself can be used as a
model organism with which to study evolutionary processes in nature.
We invite manuscripts presenting significant original research that
applies GEC to biological problems. Topics of interest include
(but not limited to):
+ Data mining biological data repositories
+ Sequence alignment
+ Phylogenetic reconstruction
+ Gene expression and regulation, alternate splicing
+ Functional diversification through gene duplication
and exon shuffling
+ Structure Prediction for biological molecules (structural
genomics and proteomics)
+ Network reconstruction for development, expression,
metabolism, catalysis, etc.
+ Dynamical system approaches to biological systems
+ Simulation of cells, viruses, organisms, and ecologies
More information about GPEM (including contents of recent issues)
can be found at
http://www.kluweronline.com/issn/1389-2576
IMPORTANT DATES
February 15, 2003: SUBMISSIONS due
April 15, 2003: REVIEWS back to authors
May 15, 2003: REVISIONS due (if necessary)
June 15, 2003: CAMERA-READY versions due
Publication is expected in 2003.
------------------------------
From: George Paliouras <[EMAIL PROTECTED]>
Subject: Postdoctoral Fellowships: Expressions of Interest
Date: Mon, 23 Sep 2002 15:46:15 +0300
Postdoctoral Fellowships: Expressions of Interest
Institute of Informatics and Telecommunications (IIT)
Software and Knowledge Engineering Laboratory (SKEL)
NCSR "Demokritos"
http://www.iit.demokritos.gr/skel/
IIT is looking for young researchers with a doctorate
degree in a informatics (or related engineering,
physical and mathematical sciences) and research
activity in one or more of the following areas,
which are of interest to the Software and
Knowledge Engineering Laboratory:
Knowledge Management / Ontologies
Semantic Web
Language Engineering
Human-Computer Interaction
User Modelling
Multimedia Information Processing
Information Retrieval
Machine Learning
Knowledge Discovery from Data / Data Mining
Multiagent Systems
with an emphasis on Web applications.
Further information about the activity of SKEL is
available in the above-mentioned Web site.
Expressions of interest should include a recent CV and
should be addressed to the head of SKEL:
Dr. Constantine D. SPyropoulos
Tel: +30-(0)10-6503196
Fax: +30-(0)10-6532175
email: [EMAIL PROTECTED]
------------------------------
From: Kai Ming Ting <[EMAIL PROTECTED]>
Subject: contribution to MLlist
Date: Tue, 08 Oct 2002 12:34:43 +1000
Two ARC Australian Postgraduate Awards Industry (APAI)
The postgraduates will work on a research project with Telstra on
developing machine learning methods to predict customer behaviour. The
scholarships are funded by the Australian Research Council, $22,771
(tax-free) per year, for three years.
The applicants will be required to be knowledgeable in the following
areas: machine learning, mathematics, statistics, econometrics,
information theory, algorithms, data mining or in a related area, and
be proficient in programming in C, C++ or Java.
More details can be obtained from:
http://www.csse.monash.edu.au/~dwa/apai.html
Closing date: November 11th 2002.
------------------------------
From: Stefan Wermter <[EMAIL PROTECTED]>
Subject: New MSc Intelligent Systems
Date: Wed, 09 Oct 2002 18:02:35 +0100
New MSc Intelligent Systems
The School of Computing and Technology, University of Sunderland
is delighted to announce the launch of its new MSc Intelligent Systems
programme for 24th February. Building on the School's leading edge
research in intelligent systems this masters programme will be
funded via the ESF scheme (see below).
Intelligent Systems is an exciting field of study for science and
industry since the currently existing computing systems have
often not yet reached the various aspects of human performance.
"Intelligent Systems" is a term to describe software systems and
methods, which simulate aspects of intelligent behaviour. The intention
is to learn from nature and human performance in order to build more
powerful computing systems. The aim is to learn from cognitive science,
neuroscience, biology, engineering, and linguistics for building more
powerful computational system architectures. In this programme a
wide variety of novel and exciting techniques will be taught including
neural networks, intelligent robotics, machine learning, natural language
processing, vision, evolutionary genetic computing, data mining,
information retrieval, Bayesian computing, knowledge-based systems,
fuzzy methods, and hybrid intelligent architectures.
PROGRAMME STRUCTURE
The following lectures/modules are available:
Neural Networks
Intelligent Systems Architectures
Learning Agents
Evolutionary Computation
Cognitive Neural Science
Knowledge Based Systems and Data Mining
Bayesian Computation
Vision and Intelligent Robots
Natural Language Processing
Dynamics of Adaptive Systems
Intelligent Systems Programming
Funding up to 6000 pounds (9500Euro) for eligible students
The Bursary Scheme applies to this Masters programme commencing
February 2003 and we have obtained funding through the European
Social Fund (ESF). ESF support enables the University to waive the
normal tuition fee and provide a bursary of £ 75 per week for 45 weeks
for eligible EU students, together up to 6000 pounds.
For further information in the first instance please see:
http://osiris.sund.ac.uk/webedit/allweb/courses/progmode.php?prog=G550A&mode=FT&mode2=&dmode=C
For information on applications and start dates contact:
[EMAIL PROTECTED] Tel: 0191 515 2758
For academic information about the programme contact:
[EMAIL PROTECTED]
------------------------------
From: Russ Greiner <[EMAIL PROTECTED]>
Subject: Alberta Ingenuity Centre for Machine Learning
Date: Wed, 16 Oct 2002 22:03:40 -0600
We are pleased to announce the creation of the new
Alberta Ingenuity Centre for Machine Learning.
This multi-year, multi-million dollar centre, located at the
University of Alberta (Edmonton), will conduct the highest quality
research in both fundamental and applied machine learning.
While we will initially focus on
* bioinformatics
* interactive entertainment (including computer games)
we are eager to extend to any other area related to Machine Learning
and Datamining.
We are currently recruiting at essentially EVERY level:
faculty members (junior or senior; even endowed chairs!)
post-doctoral fellows / research associates
graduate students -- both MSc and PhD
We also have a substantial budget to support visitors, both short and
long term.
For more information, see
http://www.aicml.ca
or contact us at
[EMAIL PROTECTED]
------------------------------
From: Jim Farrand <[EMAIL PROTECTED]>
Subject: ANN: RocOn - ROC Visualisation Tool
Date: Fri, 4 Oct 2002 10:14:26 +0100 (BST)
ROCOn is a tool to aid ROC analysis of machine learning classifiers.
Features include:
o Visualisation of a 2D ROC space
o Plots TP-rate/FP-rate of classifiers
o Can generate a spread of points for single probabalistic classifier
o Plots ROC curve and convex hull
o Runs as a standaline app or embedded in a web-page
o ROC graphs can be exported to JPEG or EPS
ROCOn is available from the University of Bristol at:
http://www.cs.bris.ac.uk/~farrand/rocon/
------------------------------
End of ML-LIST Digest Vol 14, No. 8
***********************************
