Machine Learning List
Tue, 01 Jun 2004 00:06:25 -0700
Machine Learning List: Vol. 16, No. 9
Tuesday, June 1, 2004
Contents
Calls for Papers/Participation
CFP: NeuroBotics - Bioinspired computation for Robotics
CFP: First International Workshop on Knowledge Discovery
CFP: CCS Workshop on Computer Security
CFP - Special Track at 16th ICTAI-2004
CFP: Learning and Adaptation in Games at CGAIDE 2004
CFP: Special Issue on "Learning to Improve Reasoning"
Career Opportunities
Research Position at the NASA Jet Propulsion Laboratory
job at Everest Tech
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]
To keep mailings to a manageable size, please keep submissions brief.
For meeting announcements, do highlight the meeting Web site and the
goals of the event but omit information such as the program committee
and talk schedules. Also, only first calls for papers/participation
and brief change of deadline announcements will be included. The ML
List moderator reserves the right to omit/edit submissions to meet
these criteria.
----------------------------------------------------------------------
From: Stefan Wermter <[EMAIL PROTECTED]>
Subject: CFP: NeuroBotics - Bioinspired computation for Robotics
Date: Fri, 14 May 2004 11:05:57 +0100
International Workshop on NeuroBotics:
Bioinspired Computation for Robotics
20 September 2004
Call for Papers
Substantial progress has been made recently in bio-inspired computation
and robotics. This international workshop invites contributions to
robotics which use methods of learning or artificial neural networks
and/or are inspired by observations and results in neuroscience,
cognitive science and animal behaviour.
Topics of interest include but are not restricted to:
Neural networks for robots
Biomimetic robots
Learning for robotics
Cognitive robots
Speech interfaces and neural networks
Neural Vision
Talking robots
Spiking neural networks in robots
Learning self localization and mapping
Imitation and neural networks
Cognitive Development in robots
Location
The workshop is organised as part of the 27th Conference on Artificial
Intelligence (KI-2004) and runs parallel to the 34th Annual Meeting of
the German Computer Science Society (Informatik 2004). The workshop
will take place on September 20, 2004 at the University of Ulm, Germany.
Format
We invite contributions for half-hour talks and, if possible,
additional short demonstrations. If we receive sufficient submissions
of high quality, we plan to publish the revised articles of the
workshop contributions in a journal or book.
Deadlines
Please send either abstracts of one page or full papers up to 8 pages
(and if appropriate, possible descriptions of demonstrations) until
June 9, 2004 to the organisers email address below.
You will receive notification of acceptance, a more detailed
workshop program and regarding the planned publication by July
5, 2004.
Workshop registration: until August 20, 2004.
Conference fees: will be not more than 100 Euro for the workshop.
Registration at the conference will be optional.
Organisation and more details:
More details about the AI conference can be found at:
http://ki2004.uni-ulm.de and more details about the organsing
MirrorBot project to found at
http://www.his.sunderland.ac.uk/mirrorbot/
and updates on this call at
http://www.his.sunderland.ac.uk/mirrorbot/call.html
------------------------------
From: Joao Gama <[EMAIL PROTECTED]>
Subject: CFP: First International Workshop on Knowledge Discovery
Date: Tue, 18 May 2004 14:41:43 +0100
2nd Announcement & Call for Papers
First International Workshop on Knowledge Discovery in Data Streams 24
September 2004, Pisa, Italy
http://www.lsi.us.es/~aguilar/ecml2004/
Submission deadline: June 14, 2004
in conjunction with ECML/PKDD 2004:
The 15th European Conference on Machine Learning and
The 8th European Conference on Principles and Practice of Knowledge
Discovery in Databases,
http://ecmlpkdd.isti.cnr.it/
MOTIVATION Databases are growing incessantly and many sources produce
data continuously. In many cases, we need to extract some sort of
knowledge from this continuous stream of data. Examples include
customer click streams, telephone records, large sets of web pages,
multimedia data, scientific data, and sets of retail chain
transactions. These sources are called data streams. The goal of this
workshop is to convene researchers who deal with decision rules,
decision trees, association rules, clustering, filtering,
preprocessing, post processing, feature selection, visualization
techniques, etc. from data streams and related themes. We are looking
for all possible contributions related to inductive learning from data
streams.
The goal of this workshop is to convene researchers who deal with
decision rules, decision trees, association rules, clustering,
filtering,preprocessing, post processing, feature selection,
visualization techniques, etc. from data streams and related themes.
Research works presenting theoretical results, basic research,
perspective solutions and practical developments are welcome, provided
that they address the topic of the workshop. Position papers are also
welcome and encouraged.
Topics of Interest
Topics include (but are not restricted to):
* Data Stream Models
* Clustering from Data Streams
* Decision Trees from Data Streams
* Association Rules from Data Streams
* Decision Rules from Data Streams
* Feature Selection from Data Streams
* Visualization Techniques for Data Streams
* Incremental on-line Learning Algorithms
* Mining spatio-temporal data streams
* Scalable Algorithms
* Real-Time Applications
* Real-World Applications
Important Dates
Submission deadline: June 14, 2004
Notification of acceptance: July 5, 2004
Camera-ready copies due: July 12, 2004
------------------------------
From: Philip Chan <[EMAIL PROTECTED]>
Subject: CFP: CCS Workshop on Computer Security
Date: Fri, 14 May 2004 22:08:54 -0400 (EDT)
CALL FOR PAPERS
CCS Workshop on Visualization and Data Mining for Computer Security
October 29, 2004
Wahsington, DC, USA
(George Mason University, Fairfax, VA, USA)
Held in conjunction with the Eleventh ACM Conference on
Computer and Communications Security
http://www.cs.fit.edu/~pkc/vizdmsec04/
Information about security on large and complex computer networks is
high volume, heterogeneous, distributed, and dynamic over time. Of
interest to this workshop are two complementary methods to process
high-dimensional data into knowledge: visualization and data mining.
Visualization represents high-dimension security data in 2D/3D
graphics and animations intended to facilitate quick inferences for
situational awareness and focusing of attention on potential security
events. Data mining focuses on algorithms to accurately detect
patterns in high-dimension security data representing unauthorized
system access or computer network attacks. Papers with demonstrated
results will be given priority.
Information on last year's DMSEC workshop can be found at
http://www.cs.fit.edu/~pkc/dmsec03/ .
Important Dates:
Paper submissions due: June 18, 2004
Notifications to the authors: August 6, 2004
------------------------------
From: "Ian Davidson" <[EMAIL PROTECTED]>
Subject: CFP - Special Track at 16th ICTAI-2004
Date: Tue, 25 May 2004 13:17:02 -0400
Learning at the 16th IEEE International Conference on Tools with
Artificial Intelligence (ICTAI-2004)
Call for Papers Theories and Applications of Unsupervised and
Semi-Supervised Learning
Web site: http://www.cse.fau.edu/~zhong/cfp-learning.htm
A Special Track at the 16th IEEE International Conference on Tools
with Artificial Intelligence (ICTAI-2004) November 15-17, 2004,
Marriot Hotel, Boca Raton, Florida
ICTAI 2004 Conference Web Site: http://www.cse.fau.edu/~ictai04/
Modern data mining and machine learning applications often involve
learning from large amounts of data without output (i.e., category
labels) or with only a very limited number of labels. Examples include
automatic categorization of document collections, gene function
analysis from gene expression (DNA microarray) data, hyper-spectral
image segmentation/classification, and content-based image retrieval,
etc. In these applications, category labels are usually difficult or
expensive to get, or even dynamic. Traditional classification
techniques have become insufficient in addressing these challenges;
Various unsupervised clustering and semi-supervised learning
algorithms have recently been proposed and successfully employed.
The success of unsupervised and semi-supervised learning motivates
further enhancements to existing algorithms and proposing new
algorithms to cope with the requirements of real world problems. While
typical applications have focused on clustering and classification
tasks, there is a spectrum of possible learning situations such as:
* learning from completely unlabeled data, learning from
* unlabeled data and both positively and negatively labeled
* data, learning from unlabeled and only positively (or only
* negatively) labeled data, learning from partially
* incorrectly labeled data and unlabeled data.
This special track solicits and welcomes papers in the general area of
applications of unsupervised and semi-supervised learning as well as
algorithmic enhancements to handle issues raised in real world
problems.
Topics of interest (include but are not limited to)
* Clustering with constraints Applications of clustering in
* AI Modeling of learning with partial labels Semi-supervised
* learning methods and applications Theoretical or empirical
* evaluation of the value of labeled and unlabeled data
* Comparative study of existing unsupervised and
* semi-supervised learning methods Any related applications
* (text clustering/classification, CBIR, scientific data
* analysis, data mining for intrusion detection, ......)
Key dates
June 18, 2004: Deadline for paper submission
August 2, 2004: Notification of acceptance
September 3, 2004: Camera-ready papers due
Any questions regarding this special track or the submission
procedure, please don't hesitate to contact the track co-chairs at
[EMAIL PROTECTED] or [EMAIL PROTECTED]
------------------------------
From: [EMAIL PROTECTED]
Subject: CFP: Learning and Adaptation in Games at CGAIDE 2004
Date: Mon, 24 May 2004 15:55:01 +0200
CALL FOR PAPERS
Special Track on Learning and Adaptation in Games
at the
International Conference on Computer Games:
Artificial Intelligence, Design and Education 2004
8-10 November 2004
Microsoft Campus, Reading, UK
Artificial intelligence in computer games covers the behaviour and
decision-making process of game-playing opponents. In classic
analytical games, such as chess, checkers and go, the strongest
game-playing programs rely mostly on fast search techniques, whereas
in commercial games, such as action games, role-playing games and
strategy games, the behaviour of opponents is commonly implemented as
simple rule-based systems. With a few exceptions machine-learning
techniques are rarely applied to state-of-the-art computer game
playing systems.
Machine-learning techniques may provide game-playing programs with the
ability to improve their performance by learning from mistakes and
successes, to automatically adapt to the strengths and weaknesses of a
human player, to learn from their opponents by imitating their tactics,
or to discover new knowledge by analysing game collections or perfect
move databases.
There is a relatively small group of enthusiastic researchers that
investigate the use of machine-learning techniques to enhance computer
games. Our aim is to bring them together at the CGAIDE 2004 conference,
by having a special track on "Learning and Adaptation in Games", with
a good selection of high quality papers in this research area. We also
strive to use this track to increase the computer-games industry's
awareness of machine-learning techniques.
Topics of interest: The special track on "Learning and Adaptation in
Games" will cover the application of machine-learning techniques to
all aspects of computer games. The track is limited neither to
specific types of games, nor to specific machine-learning techniques.
Submissions:
Draft paper submission : 30 July 2004
Notification of acceptance : 23 August 2004
Camera-ready submission deadline : 13 September 2004
Accepted papers will be published in the conference proceedings.
Authors of the best of the accepted papers will be invited to publish
their papers in the on-line International Journal of Intelligent Games
and Simulation (http://www.scit.wlv.ac.uk/~cm1822/ijigs.htm).
Detailed information on submitting papers is found at the CGAIDE
website at http://www.scit.wlv.ac.uk/~cm1822/cgaide.htm. Submissions
for the special track on "Learning and Adaptation in Games" can be
sent either to the conference administrator ([EMAIL PROTECTED]), or
directly to the track organisers ([EMAIL PROTECTED]).
If you have questions regarding the special track, don't hesitate to
contact the organisers.
Organisers: Pieter Spronck Institute for Knowledge and Agent
Technology, University of Maastricht [EMAIL PROTECTED]
Johannes Fuernkranz Knowledge Engineering Group, TU Darmstadt
[EMAIL PROTECTED]
------------------------------
From: Seth Rogers <[EMAIL PROTECTED]>
Subject: CFP: Special Issue on "Learning to Improve Reasoning"
Date: Fri, 21 May 2004 08:44:06 -0700
Special Issue of Computational Intelligence
on "Learning to Improve Reasoning"
Co-editors:
Seth Rogers ([EMAIL PROTECTED])
Afzal Upal ([EMAIL PROTECTED])
Machine Learning has made great strides in recent years in maturing as
a cohesive research topic and producing real-world applications, but
most of the progress has been in the sub-topics of classification and
reactive control. However, machine learning also aims to contribute in
more complex tasks that involve reasoning, planning and inference.
There has been a resurgence of interest in this area, as evidenced by
the successful symposium on "reasoning and learning in cognitive
systems" <http://www.isle.org/symposia/reason> held at Stanford on
March 21-22, 2004. The special issue will gather a sampling of recent
research on machine learning for complex, multi-step performance tasks
and present a comprehensive picture of the field. The issue would
contain fresh approaches to a variety of specific tasks that are not
already covered in the archival literature. Taken as a whole, this
will inspire researchers to renew efforts to study learning on these
more complex tasks and extend the capabilities within the current
reach of machine learning systems.
Research Areas: We welcome original and previously unpublished papers
(previous publication of partial results at a conference/workshop is
allowed) that make substantial contributions to the area of learning
for multi-step performance tasks (such as reasoning, planning, and
inference). These include papers that (a) propose novel algorithms for
learning to improve the performance of domain independent reasoning
systems, (b) review, compare, and analyze different learning paradigms
providing new insights such as relating domain features to solution
features, (c) advance evaluation techniques for measuring the
performance of learning-for-reasoning systems, and (d) analyze issues
involved in deploying the learning for reasoning systems in real world
applications. The topics of particular interest include, but are not
limited to, the following: - Machine learning for planning - Machine
learning for problem solving - Machine learning for constraint
programming - Machine learning for computer games - Speed-up learning
- Learning to improve solution quality
Review Criteria: All papers will be reviewed by at least two
experts. An ideal paper will clearly define the learning problem,
describe the proposed learning algorithm in enough detail to allow
replication by others, specify and motivate the performance
measure(s), and detail evidence that supports conclusions drawn by the
authors. All submissions should be clearly written and must discuss
relationship of the proposed research to previously published
work. Editors reserve the right to return a submission without review
if it is deemed not to address issues of interest identified in this
CFP or adhere to the formatting guidelines.
Formatting Guidelines: Manuscripts should conform to the formatting
instructions found at
<http://www.blackwellpublishing.com/submit.asp?ref=0824-7935>. Due to
the short timeline we will not be able to review submissions that are
more than 30 pages long.
Proposed Timeline:
August 1, 2004: Letter of intent to submit sent
to [EMAIL PROTECTED]
September 1, 2004: full paper submission deadline
March 1, 2005: decision
August 26, 2005: publication
Please contact [EMAIL PROTECTED] or [EMAIL PROTECTED] for
further information.
------------------------------
From: Kiri Wagstaff <[EMAIL PROTECTED]>
Subject: Research Position at the NASA Jet Propulsion Laboratory
Date: Thu, 6 May 2004 17:42:59 -0700 (PDT)
The Machine Learning Systems group at the NASA Jet Propulsion
Laboratory is seeking candidates for the following position:
Machine Learning Researcher
Requisition #: 1542
REQUIRES: B.S. degree in Physical or Computational Science with 5-10
years experience or M.S. degree in similar discipline with 3-8 years
experience or equivalent directly related experience. Demonstrated
experience in machine learning, pattern recognition techniques, and
image analysis. Ability to conduct independent research, as
demonstrated by peer-reviewed publications in professional journals
and conferences. Ability to work effectively in a small team
environment and to head and advise other members of the team in an
area of expertise. Excellent verbal and written communication skills.
Excellent programming and analytical skills.
DESIRE: Ph.D. degree in Physical or Computational Science with 1-6
years experience or equivalent directly related experience. Experience
with at least three of the following: kernel methods, time series
analysis, pattern recognition, data mining, data fusion, supervised
and unsupervised machine learning, Bayesian inference and parameter
estimation, and reinforcement learning. Active professional in the
Machine Learning research community. Willingness to work on diverse
application areas with a variety of problem solving techniques.
WILL: Perform collaborative scientific research and software
engineering in the Machine Learning Systems Group of the Exploration
Systems Autonomy Section (367). Will work both independently and as a
leader in a team setting on R&D efforts to advance the state of the
art in software to pursue the goal of enhanced autonomy and science
return from space missions. Will lead and manage independent research
tasks and work, collaborate on research tasks, and provide technical
leadership. Will obtain funding to lead and conduct research and
development by initiating and collaborating on proposals. Will
present results at professional meetings and publish work in
peer-reviewed journals.
Applicants are invited to submit a CV, brief statement of research
interests, and a list of publications to:
Rebecca Castano [EMAIL PROTECTED]
Jet Propulsion Laboratory, MS 126-347 tel: (818) 393-5344
Pasadena, CA 91109, USA fax: (818) 393-5244
------------------------------
From: "Anil Kamath" <[EMAIL PROTECTED]>
Subject: job at Everest Tech
Date: Sun, 9 May 2004 08:20:33 -0700
Everest Technologies is a start-up with a unique, patent-pending
technology to manage large complex search marketing campaigns. Our
technology is based on sophisticated mathematical modeling and
stochastic optimization techniques. We deliver search marketing
services to some of the world's biggest online advertisers. The
company was started by Stanford graduates and has many Stanford/
Berkeley grads on its team.
We are looking for an Algorithms Engineer with a strong background in
machine learning algorithms to join our team and work on large scale
complex problems in the area of search marketing. The Algorithms
Engineer will work on developing statistical models and algorithms
for real-time stochastic optimization on large scale numerical data.
Candidates should have a Masters/PhD in Computer Science or a closely
related discipline. Programming expertise and knowledge of machine
learning, data mining, randomized algorithms or statistics are also
required. Knowledge of working with large data sets, computer networks
and distributed systems is extremely helpful.
We are also looking for smart developers/interns for full/part-time
positions.
Please send resumes to [EMAIL PROTECTED]
------------------------------
End of ML-LIST Digest Vol 16, No. 9
************************************