|
We apologize for multiple postings of this message. ----------------------------------------------------------- *************************************************************************** The first
international Workshop on topological learning In
conjunction with ISMIS 2009 http://eric.univ-lyon2.fr/~topolearn/ *************************************************************************** SUBMISSION
DEADLINE HAS BEEN EXTENDED: JUNE 15, 2009 *************************************************************************** CALL FOR PAPER There is a growing interest in learning data
topology, from theoretical results to real-world applications. Topological
learning is an emerging field which is expected to bring new insights in all
the areas of the data mining and knowledge discovery process. Data mining and knowledge discovery (KDD), as stated
in their early definition, can today be considered as stable fields with
numerous efficient methods and studies that have been proposed to extract
knowledge from data. In every data mining task, understanding the structure of
multidimensional patterns, in supervised or unsupervised cases, is of
fundamental importance. Topological learning aims at finding hidden structures
(usually low-dimensional manifolds) in order to better understand and exploit
data. The aim of this workshop is to address issues related
to the concepts of learning data topology. Our goal is to attract papers
dealing with each step of this field. Actually, learning data topology within
the KDD process implies to work on every step, starting from the pre-processing
(e.g. structuring and organizing) to the visualization and interpretation of
the results, via the data mining methods themselves. There are still big practical and theoretical challenges
dealing with data topology, among which: how to deal with noisy,
high-dimensional, multi-scale or complex data (times series, images, graphs,
trees, texts, etc.)? How to interpret and qualify the results? How to select
the models complexity? How to design efficient algorithms with theoretical
guarantees? When is topological learning beneficial against usual methods? Etc.
Papers dealing with these issues and reporting applications on real data will
be of fundamental interest for this emerging field, and will take high priority
for the selection process. KDD fields which involve topological learning,
include, but are not limited to: - Topology learning - Manifold learning - Spectral clustering and embedding - Spectral feature selection - Linear and non linear dimensionality
reduction - Computational topology - Mathematical morphology - Geometric Inference - Topological learning and complex data - Applications and experience feedback More and more people approach the field of
topological learning from different and interesting angles. They come from
various communities such as data mining, statistics, geometry, topology,
physics, medicine or engineering. We believe that now is the right time to
establish and enhance cross fertilization between these communities. During
this workshop, researchers will have the opportunity to bring new ideas,
discuss their experiences and contribute to the theoretical and practical
maturation of topological learning. The workshop will consist in a series of
communications (oral presentations or poster). A reasonable time will be left
for the discussion after each presentation. All the articles will be reviewed
at least twice with a goal to improve their quality and give advice to the
authors. A dedicated place will be given to the young researchers with a
session (Position paper) grouping the work in progress in the various European
teams. That can be the occasion for a PhD student or a young researcher to
present his/her starting project. This session will be particularly significant
for work on the beginning and the installation of research groups on shared
topics. Demonstrations of research results could be associated with the poster
presentations. INTRUCTIONS FOR AUTHORS The submissions should be written in English and
should not exceed 10 pages in the Springer Verlag format. Submitted papers will be evaluated by at least three
reviewers. Any submission that exceeds length limits or deviates
from formatting requirements may be rejected without review. Papers should be submitted electronically on the
workshop website. IMPORTANT DATES Abstract submission June 8, 2009 Paper submission June 15, 2009 Notifications July 12, 2009 Camera-ready version July 31, 2009 Workshop September 14, 2009 PROGRAM CHAIRS - Michaël AUPETIT, CEA, LIST, - Hakim HACID, Alcatel-Lucent Bell Labs, - Djamel A. ZIGHED, PROGRAM COMMITTEE (to be completed..) - Michaël AUPETIT, CEA, LIST,
Saclay, France - Younès BENNANI, Univ. Paris 13, Paris, France - Stephane BONNEVAY,
Univ. Lyon 1, France - Marc BUI, EPHE, Paris - Frédéric CHAZAL, INRIA, - David COHEN-STEINER, INRIA, - Marie COTRELL, Univ. Dauphine,
Paris, France - Pierre GAILLARD, CEA, DAM, Arpajon, France - Hakim HACID,
Alcatel-Lucent Bell Labs, - Michel LAMURE, Univ. Lyon 1,
France - John LEE, UCL, Louvain-la-Neuve, Belgium - Sylvain LESPINATS, CEA,
LIST, - Thomas MARTINETZ, - Mikhail BELKIN, The - Steve OUDOT, INRIA, Orsay, France - Fabrice ROSSI, ENST, Paris, France - Vin de SILVA, Pomona College, California, USA - Michel VERLEYSEN, UCL, Louvain-la-Neuve,
Belgium - Tetsuya YOSHIDA, Hokkaido University, Japan |
-- You are currently subscribed to compgeom-announce. To unsubscribe or access the archives, go to https://lists-sop.inria.fr/wws/info/compgeom-announce