Machine Learning List Volume 19, Number 1

[Machine Learning mailing list]

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           Machine Learning List: Volume 19, Number 1
                    Monday, February 5, 2007
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Contents

Call For Papers
  International Conference on Machine Learning
  Evaluation Methods for Machine Learning II
  FUZZ-IEEE: Advances in Incremental Learning
  Demonstrations at International Conference on User Modeling
  Workshop on Data Representation Discovery
  Analysis of Genetic Representations and Operators
  Towards User Modelling and Adaptive Systems for All
  Data Analysis in BioMedicine And Pharmacology
  Natural Language Conference/Empirical Methods and Learning
  Uncertainty in Artificial Intelligence
  Personalizing Real and Virtual Explorations of Cultural Heritage
  What Went Wrong and Why
Graduate Program
  PhDs in Neuroinformatics/Computational Neuroscience in UK
Book Publication
  Two Books on Data Mining
Software Release
  ABL 1.0 Alignment-Based Learning

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Date: Jan 31, 2007
From: Pat Langley <langley@...>
Subject: International Conference on Machine Learning

Call for Papers

24th International Conference on Machine Learning
Corvallis, Oregon, USA, June 20-24, 2007,
http://oregonstate.edu/conferences/icml2007/

Abstract Submission Deadline: February 7, 2007
Full Paper Submission Deadline: February 9, 2007

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Date: Dec 6, 2006
From: William Elazmeh <welazmeh@...>
Subject: Evaluation Methods for Machine Learning II

Call for Participation

22nd National Conference on Artificial Intelligence
  Workshop on Evaluation Methods for Machine Learning II
Vancouver, BC, Canada, July 22-26, 2007
http://www.site.uottawa.ca/~welazmeh/conferences/AAAI-07/workshop/

Submission Deadline: April 1, 2007
Camera-ready Deadline: May 15, 2007
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Date: Dec 19, 2006
From: Abdelhamid Bouchachia <hamid@...>
Subject: FUZZ-IEEE: Advances in Incremental Learning

Call for Papers

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2007)
London, UK, July 23-26, 2007
Website: http://www.fuzzieee2007.org/

Tutorials/Special Sessions Deadlines: November 30, 2006
Paper Submission Deadline: January 31, 2007
Camera-ready Deadline: May 1, 2007
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Date: Dec 21, 2006
From: Alexandros Paramythis <alpar@...>
Subject: Demonstrations at International Conference on User Modeling

Call for Demonstrations

11th International Conference on User Modeling (UM07)
Corfu, Greece, June 25-29, 2007
http://www.iit.demokritos.gr/um2007/

Submission Deadline: February 23, 2007
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Date: Jan 5, 2007
From: Isabelle Guyon <isabelle@...>
Subject: Workshop on Data Representation Discovery

Call for Papers

International Joint Conference on Neural Networks (IJCNN07)
  Workshop on Data Representation Discovery
Orlando, Florida, USA, August 16, 2007
http://clopinet.com/isabelle/Projects/agnostic/

Paper Submission Deadline: January 31, 2007
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Date: Jan 9, 2007
From: Jorge Tavares <jast@...>
Subject: Analysis of Genetic Representations and Operators

Call for Papers

Analysis of Genetic Representations and Operators (ARGO 2007)
http://agro2007.dei.uc.pt
A special session of the 2007 IEEE Congress on Evolutionary Computation
Singapore, September 25-28, 2007
http://www.cec2007.org/

Paper Submission Deadline: March 15, 2007
Camera-ready Deadline: June 15, 2007
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Date: Jan 10, 2007
From: Jesus G. Boticario <jgb@...>
Subject: Towards User Modelling and Adaptive Systems for All

Call for Papers

Towards User Modelling and Adaptive Systems for All (TUMAS-A 2007)
http://adenu.ia.uned.es/workshops/um07/tumasa07/
In conjunction with User Modelling 2007 (UM '07)
Corfu, Greece, June 25-29, 2007
http://www.iit.demokritos.gr/um2007/

Submission Deadline: February 7, 2007
Camera-ready Deadline: May 1, 2007
Early Registration: March 19, 2007
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Date: Jan 11, 2007
From: Allan Tucker <cssrajt@...>
Subject: Intelligent Data Analysis in BioMedicine And Pharmacology

Call for Papers

Intelligent Data Analysis in BioMedicine And Pharmacology (IDAMAP 2007)
Amsterdam, The Netherlands, July 8, 2007
http://idamap.org/idamap2007

Paper Submission Deadline: April 9, 2007
Camera-ready Deadline: June 8, 2007
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Date: Jan 12, 2007
From: Jason Eisner <jason@...>
Subject: Natural Language Conference/Empirical Methods and Learning

Call for Papers

EMNLP-CoNLL Joint Conference 2007
  Conference on Empirical Methods in Natural Language Processing
  Conference on Computational Natural Language Learning
Prague, Czech Republic, June 28-30, 2007
http://cs.jhu.edu/EMNLP-CoNLL-2007/CFP.html

Paper Submission Deadline: March 26, 2007
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Date: Jan 14, 2007
From: Ronald Parr <parr@...>
Subject: Uncertainty in Artificial Intelligence

Call for Papers

23rd Conference on Uncertainty in Artificial Intelligence
Vancouver BC, Canada, July 19-22, 2007
http://www.cs.duke.edu/uai07/

Abstract Submission Deadline: February 28, 2007 noon EST
Full Paper Submission Deadline: March 2, 2007, noon EST
Camera-ready Deadline: May 27, 2007,  noon EDT
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Date: Jan 17, 2007
From: Ardissono Liliana <liliana@...>
Subject: Personalizing Real and Virtual Explorations of Cultural Heritage

Call For Papers

User Modeling and User-Adapted Interaction:
  The Journal of Personalization Research
  (An international journal published by Springer Verlag)
Special issue on
  Personalizing Real and Virtual Explorations of Cultural Heritage
http://www.umuai.org/

Paper Submission Deadline: May 1, 2007

Guest editors
Liliana Ardissono <liliana@...>
Daniela Petrelli <d.petrelli@...>
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Date: Thu, 25 Jan 2007 15:02:56 -0800
From: Daniel Shapiro <dgs@...>
Subject: What Went Wrong and Why

Call for Anecdotes

AI Magazine Special Issue on What Went Wrong and Why:
Lessons from AI Research and Application

AI Magazine will soon publish a special issue on the topic of
learning from mistakes, dedicated to the proposition that insight
often begins with unexpected results, and arrives in the response to
apparent problems. We believe every person working in the field of
Artificial Intelligence has experienced this effect, so we are
collecting anecdotes for publication.

Authors should submit 400 word descriptions of personal experiences
that link problems to insights/lessons learned. Problems can include,
but are not limited to: unusual observations, odd algorithm behavior,
technology/application mismatch, risk to people, products, projects,
or corporations, and physical systems failure. The lessons learned may
be technical, methodological, commercial, or organizational in nature,
and more. The ideal contribution will be crisp (possibly in the form
of an "A-ha!" moment), of general interest, and related to some aspect
of AI. Humor is a plus. Selected anecdotes will be published as
sidebars in the special issue.

Please send contributions to Dan Shapiro (dgs at stanford.edu) or
Mehmet Goker (mehmet.goker at us.pwc.com) by March 1, 2007 in text,
postscript, pdf, or MSWord format.
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Date: Dec 19, 2006
From: James A. Bednar <jbednar@...>
Subject: PhDs in Neuroinformatics/Computational Neuroscience in UK

4 YEAR PhD in Neuroinformatics and Computational Neuroscience at
the University of Edinburgh

We invite applications for the EPSRC/MRC funded PhD programme in
Neuroinformatics/Computational Neuroscience at the University of
Edinburgh, UK.

This is a 4 year programme with a strongly interdisciplinary character
and is ideal for students who want to apply their computational and
analytical skills to problems in neuroscience and related fields. The
first year consists of courses in neuroscience and informatics, as well
as projects based in experimental labs. The first year is followed by
a 3 year PhD project. The PhD project is commonly done in collaboration
with one of the many departments and institutes affiliated with the DTC.

The DTC programme is made up of three themes:

1) Computational and Cognitive Neuroscience.
   Computational, mathematical, and experimental studies of information
   processing in the nervous system.
2) Neuromorphic Engineering and Robotics.
   Artificial sensor perception, neuromorphic modelling, spiking
   computation, and neurorobotics.
3) Data Analysis and Systems.
   Imaging data analysis and machine learning, Bayesian methods,
   and building neurally inspired software.

Edinburgh has a strong research community in these areas and leads
the UK in creating a coherent programme in neuroinformatics.
Edinburgh has been voted as 'best place to live in Britain',
and has many exciting cultural and student activities.

Students with a strong background in computer science, mathematics,
physics, or engineering are particularly welcome to apply. Motivated
students with other backgrounds will also be considered.

About 8 full studentships are available to UK students and a small
number of EU students. The stipend is about 12,000 GB pounds per
annum. Applicants from outside the EU will need to provide their
own funding and evidence thereof.

Full info and application forms can be obtained from:
http://www.anc.ed.ac.uk/neuroinformatics/

Applications received by March 30th, 2007 will receive priority
treatment.
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Date: 18 Jan 2007
From: Cambridge University Press <cambridge@...>
Subject: Two Books on Data Mining

AN AUTHORITATIVE GUIDE TO STATE-OF-THE-ART
TECHNIQUES IN TEXT MINING AND LINK DETECTION

The Text Mining Handbook
  Advanced Approaches in Analyzing Unstructured Data
Ronen Feldman, ClearForest Corp, Waltham, MA and Bar-Ilan
University, Israel
James Sanger, ABS Ventures, Boston, MA
ISBN-13: 9780521836579
http://scientific-direct.net/c.asp?id=644144&c=46a376cc4f402c6d&l=1

THE ESSENTIAL NEW INTRODUCTION TO CLUSTERING

Introduction to Clustering Large and High-Dimensional Data
Jacob Kogan, University of Maryland, Baltimore
ISBN-13: 9780521617932
http://scientific-direct.net/c.asp?id=644144&c=46a376cc4f402c6d&l=2
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Date: Dec 18, 2006
From: Menno van Zaanen <menno@...>
Subject: ABL 1.0 Alignment-Based Learning

Software release:
ABL 1.0 Alignment-Based Learning

Alignment-Based Learning (ABL) is a symbolic grammar inference
framework that has successfully been applied for several unsupervised
machine learning tasks in Natural Language Processing (NLP). Given
sequences of symbols only, a system that implements ABL induces
structure by aligning and comparing the input sequences. As a result,
the input sequences are augmented with the induced structure.

More information on the underlying ABL system and its implementation
can be found in the following publications:

* "Bootstrapping Structure into Language: Alignment-Based Learning",
  Menno van Zaanen, 2001,
  PhD Thesis, School of Computing, University of Leeds, UK.
  http://www.ics.mq.edu.au/~menno/personal_files/docs/t_leeds.ps.gz
  http://www.ics.mq.edu.au/~menno/personal_files/docs/t_leeds.pdf
* "Implementing Alignment-Based Learning",
  Menno van Zaanen, 2002,
  In: Proceedings of the International Colloquium on Grammatical
  Inference (ICGI), pp 312-314, Amsterdam, the Netherlands.
  http://springerlink.metapress.com/openurl.asp?genre=article&
   issn=0302-9743&volume=2484&spage=312
* "String alignment in grammatical inference: what suffix trees can do",
  Jeroen Geertzen, 2003,
  Technical report ILK-0311, Tilburg University, The Netherlands.
  http://ilk.uvt.nl/downloads/pub/papers/ilk0311.pdf

The package contains a C++ implementation of ABL and a reference guide
(in texinfo) and the text of the license. The package should be easy
to install on Linux/UNIX systems using configure.

The latest version of this
package can be found on:

     http://www.ics.mq.edu.au/~menno/research/software/abl/

For questions, remarks, bugs, improvements, or other matters related
to this package, send email to Menno van Zaanen <menno@...>.
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End of ML-LIST Digest Vol 19, No. 1

[David Weiss]

The following book gives a rather powerful theory of representational
structure needed for machine learning.
David Weiss


A Generative Theory of Shape
Michael Leyton
Springer-Verlag

The purpose of the book is to develop a generative theory that has two properties regarded as fundamental to intelligence – maximizing reusability of structure and maximizing recoverability of the generative operations. These two properties are particularly important in the representation of complex organization – which is the main concern of the book. The primary goal of the theory is the conversion of complexity into understandability. For this purpose, a mathematical theory is presented of how understandability is created in a structure. This is achieved by developing a group-theoretic approach to formalizing reusability and recoverability. To handle highly complex structure, a new class of groups is invented, called unfolding groups. These unfold structure from a maximally collapsed version of that structure. A principal aspect of the theory is that it develops a new algebraic formalization of major object-oriented concepts such as inheritance. The consequence that the book establishes a representational language for complex organizational structure, that is interoperable by virtue of the principles on which the theory is based: reusability and recoverability.

The book gives extensive applications of the theory to CAD/CAM, human and machine vision, robotics,  software engineering, and physics. For example, the theory is used to give new and detailed insights into the main stages of mechanical CAD/CAM: part-design, assembly and machining. And within part-design, an extensive analysis is given of sketching, alignment, dimensioning, resolution, editing, sweeping, feature-addition, and intent-management. In robotics, several levels of analysis are developed for manipulator structure and kinematics.  In software, a new theory is given of the principal factors such as text and class structure, object creation cloning and modification, as well as inheritance and hierarchy prediction. In physics, a new theory is given of the conservation laws, and motion decomposition theorems in classical and quantum mechanics. In perception, extensive theories are developed for Gestalt grouping criteria, orientation and form, the prototype phenomena, and the main Gestalt motion phenomena (induced motion, separation of systems, the Johannson relative/absolute motion effects.  


Springer-Verlag allows the book to be viewed on-line at institutions that have a Springer subscription: On-line link: http://www.rci.rutgers.edu/~mleyton/homepage.htm