Machine Learning List: Volume 18, Number 2

Machine Learning List: Volume 18, Number 2, Monday, February 13, 2006

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Contents
Calls for Papers & Participation
   ECML/PKDD-06 Berlin
   ECML/PKDD-2006 Call for Tutorials and Workshops
   Workshop on Machine Learning in Structural and Systems Biology
   SIGKDD Explorations: Successful Real-World Data Mining Applications
   SEAL06
   Analysis of Environmental Data
   ACM SIGKDD 2006
   June 26-29, WORLDCOMP'06 An International Symposium
   ECAI06 Workshop on Neural-Symbolic Learning and Reasoning
   FOCA@ESSLLI 2006
   "Multi-objective Machine Learning" deadline extension
   KES2006 deadline extension
   Special issue of Data Mining and Knowledge Discovery
   IDAMAP 2006
Career Opportunities
   PhD research assistantships in machine learning at OGI
   Open position at ISLE

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The Machine Learning List is moderated. Contributions should be relevant
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To keep mailings to a manageable size, please keep submissions brief.
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criteria.

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Date: Wed, 18 Jan 2006 16:29:30 +0100 (MET)
From: juffi@...
To: ml@...
Subject: ECML/PKDD-06 Berlin

Call for Papers ECML/PKDD-2006 http://www.ecmlpkdd2006.org/ Berlin,
Germany, September 18-22, 2006

The 17th European Conference on Machine Learning (ECML) and the 10th
European Conference on Principles and Practice of Knowledge Discovery in
Databases (PKDD) will be co-located in Berlin, Germany, September 18-22,
2006. The combined event will comprise presentations of contributed
papers and invited speakers, a wide program of workshops and tutorials,
and a discovery challenge.

Abstract Submission deadline: 26 April 2006
Paper Submission deadline: 03 May 2006
Acceptance Notification: 14 June 2006
Camera-ready copies due: 30 June 2006

The papers must be in English and must be formatted according to the
Springer-Verlag Lecture Notes in Artificial Intelligence guidelines.
Authors instructions and style files can be downloaded at
http://www.springer.de/comp/lncs/authors.html. The maximum length of
papers is at most 12 pages in this format.

Double submissions to the KDD conference are allowed.
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Date: Mon, 30 Jan 2006 15:14:04 +0200 (EET)
From: Tapio Elomaa <elomaa@...>
To: ml@...
Subject: ECML/PKDD-2006 Call for Tutorials and Workshops

The 17th European Conference on Machine Learning (ECML) and
The 10th European Conference on Principles and Practice of Knowledge
    Discovery in Databases (PKDD)
Berlin, Germany, Sept. 18-22, 2006
http://www.ecmlpkdd2006.org/.

The ECML/PKDD-2006 Organizing Committee invites proposals for tutorials
and workshops that will be co-located with the main ECML/PKDD-2006
conference. We invite proposals for half-day tutorials and full day
workshops.

Workshop Dates
Proposal deadline                       March 31, 2006
Acceptance notification                 April 21, 2006
Call for Papers on the web              May 5, 2006
Paper submission deadline               June 28, 2006
Proceedings (camera-ready)              August 16, 2006

Tutorial Dates
Proposal deadline                       March 31, 2006
Acceptance notification                 April 21, 2006
Tutorial summary on the web             May 15, 2006
Tutorial notes (camera-ready)           August 16, 2006

For more information and detailed instructions on proposing a tutorial
or a workshop see http://www.ecmlpkdd2006.org/workshops.htm or contact
the tutorial and workshop chairs.
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Date: Sat, 21 Jan 2006 19:46:59 +0200
From: Esa Pitknen <epitkane@...>
To: ml@...
Subject: Workshop on Machine Learning in Structural and Systems Biology

CALL FOR PAPERS
Workshop on Probabilistic Modeling and Machine Learning in Structural
    and Systems Biology
http://www.cs.helsinki.fi/group/bioinfo/events/pmsb06/
Tuusula, Finland, 17-18 June 2006

The increasing amount of biological data, the development of genome-wide
measurement technologies, and the shift from the study of individual
genes to systems view all contribute to the need to develop computational
techniques for learning models from data. At the same time, the increase
in computational resources has enabled the adoption of more realistic
modeling methods.

The aim of this workshop is to provide a broad look at the state of
the art in the probabilistic modeling and machine learning methods
that involve biological structures and systems, and to bring together
method developers and experimentalists working on the problems.
We encourage submissions that bring forward methods for discovering
complex structures (e.g. interaction networks, molecule/cellular
structures) and methods supporting genome-wide data analysis.

A non-exhaustive list of topics suitable of this workshop include:

Methods:
Algorithms, Bayesian methods, Data integration/fusion, Feature/subspace
selection, High-throughput methods, Kernel methods, Machine learning,
Probabilistic inference, and Structured output prediction.

Applications:
Sequence annotation, Gene expression, Gene networks, Gene prediction,
Metabolic profiling, Metabolic reconstruction, Protein structure
prediction, Protein function prediction, and Protein-protein interaction
networks.

We invite submissions of extended abstracts, no more than four pages,
formatted according to the Springer Lecture Notes in Computer Science
style, to the email address juho.rousu at cs.helsinki.fi

Abstract submission deadline            April 23, 2006
Notification of acceptance              May 7, 2006
Final version due                       May 31, 2006
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Date: Sun, 22 Jan 2006 12:51:10 -0700
From: Osmar Zaiane <zaiane@...>
To: zaiane@...
Subject: SIGKDD Explorations: Successful Real-World Data Mining
Applications

SIGKDD Explorations 2006 June Issue
Call for Papers
Special Issue on Successful Real-World Data-Mining Applications

Submissions                             April 3, 2006
Notification and Reviews                May 1, 2006
Camera-ready due                        May 15, 2006
Issue Publication                       June 2006

This special issue invites submissions concerning successful application
of data-mining techniques in industry, from marketing to drug research.
Papers should report on real-world data-mining projects that showcase
choices, strategies, success, and failure.

>From its inception, the field of data mining has been guided by the need
to solve practical problems. Successful cases of data-mining application
in the industry are motivation and inspiration not only to industry but
also to research. This special issue will highlight some of the best
deployed data-mining systems.

Most operational industrial and scientific systems that involve data
mining to some extent are likely to be acceptable. Systems that are
responsible for mission critical systems, medical applications, cash
flow, or applications that significantly benefit humanity will be
particularly good candidates. If you are unsure about the suitability
of your paper, please contact the editors with at the email address
indicated below.

Topics include but are not limited to: Genomics; Inventory control;
Customer relationship management; ShopBots; Recommendation systems;
Auction trading systems; Clinical patient monitoring; Seismic data
interpretation; Survival analysis for medical procedures; Climate
analysis; Correlating genes with disease; Dangerous drug interactions;
Law enforcement applications; Search engine marketing; Food spoilage
elimination; Price optimization; Data visualization in mission-critical
user interfaces; Text processing.

Submissions should be made to zaiane[_at_]cs.ualberta.ca, preferably
in a PDF format and should not exceed 8 pages. In addition, please
email the abstract in text format.

Detailed formatting instructions are available from
http://www.acm.org/sigs/sigkdd/explorations/submissions.php
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Date: Tue, 24 Jan 2006 22:33:03 +0800
From: Wenjian Luo <wjluo@...>
To: ml@...
Subject: SEAL06

The Sixth International Conference on Simulated Evolution And Learning
15-18 October 2006, Hefei, Anhui, China
http://nical.ustc.edu.cn/seal06/

Evolution and learning are two fundamental forms of adaptation. SEAL'06
is the sixth biennial conference in the successful series that aims at
exploring these two forms of adaptation and their roles and interactions
in adaptive systems. Cross-fertilization between evolutionary learning
and other machine learning approaches, such as neural network learning,
reinforcement learning, decision tree learning, and fuzzy system
learning, will be strongly encouraged by the conference. The other
major theme of the conference is optimization by evolutionary and
other nature inspired approaches.

All accepted papers that are presented at the conference will be
included in the conference proceedings, published by Springer in their
Lecture Notes in Computer Science series. The best papers will be
invited to submit extended results to special issues of Genetic
Programming and Evolvable Machines, Connection Science, International
Journal of Computational Intelligence and Applications, and Journal
of Computer Science and Technology.

Special sessions and tutorials will be organized at the conference.
The conference is calling for special session and tutorial proposals.
The tutorials will be offered on 15 Oct 2006.
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Date: Wed, 25 Jan 2006 19:23:52 +0100 (CET)
To: undisclosed-recipients@...
From: aedml@... (AEDML)
Subject: Analysis of Environmental Data

You are cordially invited to attend the international seminar on
ANALYSIS OF ENVIRONMENTAL DATA WITH MACHINE LEARNING METHODS
27 February - 2 March 2006, Ljubljana, Slovenia
http://www-ai.ijs.si/SasoDzeroski/aep/aep.html

Organized by Jozef Stefan Institute, Ljubljana, in cooperation with
University of Ljubljana and Nova Gorica Polytechnic

The seminar will give an introduction to selected machine learning
methods as well as illustrative case studies of using these methods to
analyze environmental data, such as modeling algal growth in lakes and
lagoons, analyzing the influence of physical and chemical parameters on
selected bioindicator organisms, and predicting the biodegradability of
chemical compounds. The participants will learn to use selected machine
learning tools and will have the opportunity for practical work with
these tools on real environmental data. The machine learning methods and
tools introduced are applicable to data analysis problems from different
areas.

The seminar is intended for researchers and other professionals in the
areas of biology, chemistry, environmental science, and other areas
related to ecology and environmental management, whose work requires the
analysis of environmental data or modeling ecological processes. For
graduate students of the School of Environmental Sciences, Nova Gorica
Polytechinc (and cooperating universities) the seminar counts as a
specialized elective subject (9 ECTS points).

Contents:
- Introduction to machine learning: Data mining and knowledge discovery;
   Evaluating classifiers; Instance-based learning; Introduction to
   decision trees; Learning classification and regression trees; Learning
   classification rules; Naive Bayesian classification; Machine discovery
   of equations; Selecting and combining classifiers
- An overview of environmental applications of machine learning: Analysis
   of the influence of environmental factors on respiratory diseases;
   Analysis of the influence of soil habitat features on the abundance of
   Collembola; Modeling phytoplankton growth; Modeling interactions among
   red deer population, meteorological parameters and new forest growth;
- Case studies of using machine learning to analyze ecological data:
   Analysis of water quality data; Modeling algal growth in the Lagoon of
   Venice and Lake Bled; Predicting biodegradability of chemical compounds;
   Runoff prediction from rainfall and past runoff
- Demonstrations/hands-on exercises/practical work with machine learning
   software packages on real ecological data and individual consultations
   with lecturers
- Participant presentations and discussion
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Date: Wed, 25 Jan 2006 14:44:35 -0800 (PST)
From: Dimitrios Gunopulos <dg@...>
To: ml@...
Subject: ACM SIGKDD 2006

KDD-2006 CALL FOR RESEARCH PAPERS
THE TWELFTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY
AND DATA MINING
August 20-23, 2006 in Philadelphia, PA
http://www.kdd2006.com

The ACM SIGKDD conference solicits papers on all aspects of knowledge
discovery and data mining.  Areas of interest include, but are not
limited to:

Applications of data mining (biomedicine, business, e-commerce,
defense); Data and result visualization; Data warehousing; Data mining
for community generation, social network analysis, and graph-structured
data; Foundations of data mining; Interactive and online data mining;
KDD framework and process; Mining data streams; Mining high-dimensional
data; Mining sensor data; Mining text and semi-structured data; Mining
multi-media data; Mining uncertain or fuzzy data; Novel data-mining
algorithms; Privacy and data mining; Robust and scalable statistical
methods; Pre-processing and post-processing for data mining; Security
issues; Spatial and temporal data mining

Electronic abstract submission: March 3, 2006
Electronic paper submission: March 10, 2006
Author notification: May 23, 2006

Abstracts and full papers must be submitted electronically at the
conference Web site (see URL above). Templates are available at
http://www.acm.org/sigs/pubs/proceed/template.html. Papers must be
submitted in PDF format. Authors are solely responsible for ensuring
that their submissions display and print properly.

All papers will be judged based on their technical merit, significance,
originality, relevance to KDD, and presentation clarity. Papers should
describe original work that has not been published before, is not under
review elsewhere, and will not be submitted elsewhere during KDD-2006's
review period.

A separate call is being issued for industrial/government track papers;
see the conference URL above. Calls for workshop, tutorial, and panel
proposals can also be found at the conference Web site.
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Date: Sun, 29 Jan 2006 09:26:41 +0100
From: Pascal Hitzler <hitzler@...>
To: ml@...
Subject: ECAI06 Workshop on Neural-Symbolic Learning and Reasoning

Second International Workshop on Neural-Symbolic Learning and Reasoning
A Workshop at ECAI2006, Riva del Garda, Italy
August 28 or 29, 2006
http://www.neural-symbolic.org/NeSy06/

Artificial Intelligence researchers continue to face huge challenges
in their quest to develop truly intelligent systems. The recent
developments in the field of neural-symbolic integration bring an
opportunity to integrate well-founded symbolic artificial intelligence
with robust neural computing machinery to help tackle some of these
challenges.

The Workshop on Neural-Symbolic Learning and Reasoning is intended to
create an atmosphere of exchange of ideas, providing a forum for the
presentation and discussion of the key topics related to neural-symbolic
integration.

Topics of interest include:

The representation of symbolic knowledge by connectionist systems;
Learning in neural-symbolic systems; Extraction of symbolic knowledge
from trained neural networks; Reasoning in neural-symbolic systems;
Biological inspiration for neural-symbolic integration; Applications
in robotics, semantic web, engineering, bioinformatics, etc.

Researchers and practitioners are invited to submit original papers that
have not been submitted for review or published elsewhere. Submitted
papers must be written in English and should not exceed 6 pages in the
case of research and experience papers, and 2 pages in the case of
position papers (including figures, bibliography and appendices) in
ECAI format. Papers must be submitted directly by email in PDF format
to nesy@...

Deadline for submission: 15th of April, 2006
Notification of acceptance: 10th of May, 2006
Camera-ready paper due: 17th of May, 2006
ECAI 2006 main conference dates: Aug. 28th to Sept. 1st, 2006.

General questions concerning the workshop should be addressed to
nesy@....

You are also invited to subscribe to the neural-symbolic integration
mailing list at http://www.aifb.uni-karlsruhe.de/mailman/listinfo/nesy
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Date: Tue, 31 Jan 2006 09:26:35 +0100
From: Yaochu.Jin@...
To: ml@...
Subject: Multi-objective Machine Learning - deadline extension

Call for Papers Special Session on "Multi-objective Machine Learning"
2006 International Joint Conference on Neural Networks (part of WCCI'06)
July 16-21, Vancouver, Canada
http://www.wcci2006.org/

The submission deadline has been extended to February 15, 2006

Machine learning usually has to achieve multiple targets, which are
often conflicting with each other. For example in feature selection,
minimizing the number of features and the maximizing feature quality
are two conflicting objectives. It is also well realized that model
selection has to deal with the trade-off between model complexity
and approximation or classification accuracy. Traditional learning
algorithms attempt to deal with multiple objectives by combining them
into a scalar cost function so that multi-objective machine learning
problems are reduced to single-objective problems.

Recently, there has been increasing interest in applying Pareto-based
multi-objective optimization to machine learning, particularly inspired
by successful developments in evolutionary multi-objective optimization.
The multi-objective approach is particularly successful in: 1) improving
the behavior of traditional single-objective machine learning methods;
2) generating diverse multiple Pareto-optimal models for constructing
ensembles; and 3) in achieving desirable trade-offs between accuracy
and interpretability of neural networks or fuzzy systems.

This proposed special session aims to further promote research on
multi-objective machine learning by presenting the most recent research
results and discussing the challenges in this area. Topics include,
but are not limited to: multi-objective clustering, feature extraction
and feature selection; multi-objective model selection to improve
the performance of learning models, such as neural networks, support
vector machines, decision trees, and fuzzy systems; multi-objective
model selection to improve the interpretability of learning models,
e.g., to extract symbolic rules from neural networks, or to improve
the interpretability of fuzzy systems; multi-objective generation of
learning ensembles; multi-objective learning to deal with tradeoffs
between plasticity and stability, long-term and short-term memories,
specialization and generalization; multi-objective machine learning
applications

All special session papers must be submitted no later than January 31,
2005 through the conference web page at
http://139.78.75.247/WCCI-Web_paper_submit.html.
Please choose "S.Special Sessions, Sa: Multi-objective machine learning"
as your main research topic.
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Date: Fri, 3 Feb 2006 17:05:17 +0100 (CET)
From: info@...
To: ml@...
Subject: KES2006 deadline extension

KES2006, the 10th International Conference on Knowledge Based &
Intelligent Information & Engineering Systems, Bournemouth, UK
October 9-11, 2006
http://kes2006.kesinternational.org

GENERAL TRACK SUBMISSION DEADLINE EXTENDED In response to many requests
to allow more time for submission at this busy time the deadline has
been extended to 15 February 2006.

KES2006 will be the latest in the well-established KES International
conference series, celebrating a decade of bringing the results of
intelligent systems research to the international research community.
We are confident that this 10th anniversary will be a very special
event.  The conference will consist of plenaries, oral presentation
sessions, invited sessions and workshops on the applications, tools
and theory of intelligent systems.

Papers are invited from prospective authors with interests in the
indicated conference topics and related areas of application. All
contributions should be original and not published elsewhere or
submitted for publication during the review period. Please see the
Web site for details of the required paper format. To ensure high
quality, all papers must be submitted using the PROSE online system,
and will be thoroughly reviewed by the KES2006 Program Committee.
The conference proceedings will be published by a major publisher.
Extended versions of selected papers will be considered for publication
in the KES Journal http://www.kesinternational.org/journal/. Authors
will be limited to one paper per registration.

Scientists, engineers and researchers who would like to organize
an invited session of 5/6 papers, or a parallel workshop of a half or
full day, on some topic falling within the scope of the conference,
are invited to contact the KES Secretariat enclosing the title and
content of the proposed session.  We also welcome suggestions for
other activities that will appeal to our delegates.

Submission of papers: 15 February 2006
Notification of acceptance: 1 April 2006
Final papers to be received by: 1 May 2006

Proposals for Invited Sessions / Workshops: 1 February 2006
Session Chair sets Invited Session interim deadlines.
Final papers for Invited Sessions musts be received by: 1 May 2006
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Date: Sun, 5 Feb 2006 12:36:45 -0600
From: Maytal Saar-Tsechansky <Maytal.Saar-Tsechansky@...>
To: ml@...
Subject: Special issue of Data Mining and Knowledge Discovery

Call for Papers on Utility-Based Data Mining
Special issue of Data Mining and Knowledge Discovery

Data mining has made a profound impact on business practices and
knowledge management in recent years. Business intelligence has emerged
as one of the most popular applications of many data mining techniques.
However, as our understanding of data mining improved, it became clear
that in order to allow data mining to further its impact on business
applications, it would be necessary to align the data mining process and
algorithms with the broad economic objectives of the tasks supported by
data mining.

All the different stages of the data mining process impact the ultimate
economic utility derived from the data mining product. The economic
utility of acquiring data, extracting a model, and applying the
acquired knowledge must be considered. For example, in the data
acquisition phase the costs of obtaining informative and accurate data
may be considered to help identify the most cost-effective information.
Similarly, economic utility also impacts the assessment of the
decisions made based on the learned knowledge. Simple assessment
measures like predictive accuracy have given way to economic measures,
such as profitability and return on investment.

Utility-based data mining is a broad topic that covers all aspects of
economic utility in data mining. As such, it encompasses the work in
cost-sensitive learning and active learning as well as work on the
detection of rare events of high value (e.g., anomaly detection).
This issue will provide a forum for timely, in-depth presentation of
recent advances in utility-based data mining. While economic utility
considerations have played a much greater role in predictive data
mining tasks, we also encourage papers on the use of economic utility
in descriptive tasks.

We solicit high-quality, original papers describing work on the
following non-exhaustive list of topics: Cost-sensitive learning;
Active learning and information acquisition for model induction;
Pattern extraction algorithms that incorporate utility considerations;
Interaction of economic/utility considerations between various steps
in the data mining process (e.g., does misclassification cost affect
what type of data should be purchased); Types of economic factors
(utility considerations) in data mining and their trade-off;
Applications that take into account utility considerations

Please follow the submission guidelines:
https://www.editorialmanager.com/dami

Submission Deadline:  July 17, 2006
Author Notification:  October 9, 2006
Camera-ready copy due:  December 12, 2006
Special Issue publication: First half 2007
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Date: Mon, 6 Feb 2006 11:15:30 +0100
From: Niels Peek <n.b.peek@...>
To: ml@...
Subject: IDAMAP 2006

Intelligent Data Analysis in Biomedicine And Pharmacology
August 25-26, 2006, A two-day workshop in Verona, Italy
http://idamap.org/idamap2006

Submission:   May 1, 2006
Notification: June 11, 2006
Camera-ready: July 1, 2006

The IDAMAP workshop series is devoted to computational methods for data
analysis in medicine, biology and pharmacology that present results
of analysis in a form communicable to domain experts that utilizes
knowledge of the domain. Typical methods include data visualization,
data exploration, machine learning, and data mining. This year's
IDAMAP will focus attention on methods for handling temporal data.

Topics include, but are not limited to: data mining and machine learning
techniques for supervised and unsupervised learning problems; exploiting
domain knowledge in learning and data analysis; data visualization and
exploration; analysis of large data sets and relational data mining;
knowledge management and its integration with intelligent data analysis
techniques; and integration of intelligent data analysis techniques
within biomedical information systems. Specific attention will be spent
on methods for analyzing temporal data, such as: qualitative and
quantitative methods for temporal data abstraction; biomedical time
series analysis; and analyzing and interpreting longitudinal data.

Submitted papers should demonstrate how a select methodology may help
solve relevant medical problems and address: the medical or clinical
problem being addressed; the availability of prior knowledge; how
this knowledge was utilized in the data analysis or interpretation
of results; and how the newly discovered knowledge may be utilized.
Contributions discussing specific applications of intelligent data
analysis techniques are invited, and may cover analysis of medical
and health-care data, data from clinical bioinformatics data bases,
analysis of pharmacological data, drug design, drug testing, and
outcomes analysis. We also invite developers of data analysis tools
to submit papers that describe their tool and give a demonstration
during the workshop. These papers should describe the underlying
methodology of the tool, sketch the potential for application in the
field of intelligent data analysis in biomedicine, and describe a case
study in which the tool was used.

Authors should send an electronic submission in PDF format to both
chairs (n.b.peek@..., carlo.combi@...); please use "IDAMAP
SUBMISSION YOUR_NAME" as a subject, where YOUR_NAME is the surname of
the first author. Formatting instructions and instructions for authors
are on the website.
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Date: Sat, 28 Jan 2006 16:31:47 -0800
From: Miguel Carreira-Perpinan <miguel@...>
To: ml@...
Subject: PhD research assistantships in machine learning at OGI

PHD RESEARCH ASSISTANTSHIPS IN ADAPTIVE SYSTEMS AT THE OGI SCHOOL OF
SCIENCE AND ENGINEERING AT OHSU

Individuals interested in pursuing a PhD in Machine Learning or
Computational Neuroscience at OGI are eligible for research
assistantships in the Adaptive Systems Laboratory at the OGI School
of Science & Engineering (http://adsyl.csee.ogi.edu). The Laboratory,
which is part of the Department of Computer Science & Electrical
Engineering, carries out research in the areas of machine learning,
adaptive signal processing and computational neuroscience. Close ties
also exist with the Center for Spoken Language Understanding, the
Department of Biomedical Engineering at OGI, the Neurological Sciences
Institute, and the OHSU Medical School.

One of the four schools of Oregon Health & Science University, OGI is
located 12 miles west of Portland, Oregon, in the heart of the Silicon
Forest. Portland's extensive high-tech community, diverse cultural
amenities and spectacular natural surroundings combine to make the
quality of life here extraordinary. To learn more about the department,
OGI, OHSU, and Portland, please visit http://www.csee.ogi.edu.

Applicants should have a university degree in an area such as computer
science, electrical engineering, physics or mathematics, and solid
mathematical and programming skills. Background in machine learning,
image/speech processing or computer vision is highly desirable. The
assistantships cover tuition, a competitive stipend, and travel to
research conferences. Students of any nationality may apply.

Informal inquiries can be made by sending email (with supporting CV and
a statement of research interests) to adsyl-inquiry@... or to
the appropriate faculty member. For information on submitting a full
application to the PhD program in Computer Science, see the OGI
admissions information at http://www.ogi.edu/admissions.
------------------------------------------------------------------------

Date: Wed, 8 Feb 2006 9:31:56 PM PST
From: Pat Langley <langley@...>
To: ml@...
Subject: Open position at ISLE

The Institute for the Study of Learning and Expertise (ISLE) has an
opening for a postdoctoral researcher or masters-level programmer for
a new project on learning hierarchical task networks from traces of
expert behavior. ISLE is a nonprofit research company based in Palo
Alto, California, that has strong ties with Stanford University's
Center for the Study of Language and Information.

Applicants should have experience with relational approaches to
machine learning, such as explanation-based methods or inductive
logic programming, as well as an interest in techniques that combine
reasoning with learning to acquire complex structures in the presence
of background knowledge. Experience of LISP and/or Prolog would be
an asset, whereas a commitment to building AI systems and evaluating
their behavior on challenging domains is essential.

This start date for this position is July 1, 2006. To apply, send
electronic mail to Pat Langley <langley@...>. The Web site
at http://www.isle.org/ provides information about the Institute's
ongoing research activities.
____________________________________
End of ML-LIST Digest Vol 18, No. 2
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