IUBio Biosequences .. Software .. Molbio soft .. Network News .. FTP

[Computational-biology] MLDM 2007

schatte at ibai-institut.de schatte at ibai-institut.de
Thu May 18 07:52:00 EST 2006


First Call for Papers
5th IAPR International Conference on 
Machine Learning and Data Mining
MLDM´2007
 
July 4-6, 2007, Leipzig/Germany

Chair

Petra Perner
Institute of Computer Vision and applied Computer Sciences, IBaI
Leipzig/Germany

Program Committee


 <http://www.idi.ntnu.no/%7Eagnar/eng.html> Agnar Aamodt
NTNU/Norway
 <http://www.icgst.com/gvip/Leaders/D_Karras.html> Dimitrios A. Karras
Chalkis Institute of Technology/Greece
 

 <http://www.inria.fr/personnel/Nozha.Boujemaa.en.html> Nozha Boujemaa
INRIA/France
 <http://www.cs.concordia.ca/%7Ekrzyzak/> Adam Krzyzak
Concordia University, Montreal/Canada
 

 <http://www.maxbramer.org/> Max Bramer
University of Portsmouth/UK
 <http://www.cis.temple.edu/%7Elatecki/> Longin Jan Latecki
Temple University Philadelphia/USA
 

 <http://iamwww.unibe.ch/%7Efkiwww/staff/bunke.html> Horst Bunke
University of Bern/Switzerland
 <http://www.itee.uq.edu.au/%7Elovell> Brian Lovell
University of Queensland/Australia
 

 <http://isl.cudenver.edu/cios.htm> Krzysztof Cios
University of Colorado/USA
 <http://www.tsi.enst.fr/%7Epesquet/> Béatrice Pesquet-Popescu
Ecole Nationale des Télécommunications/France
 

 <http://www-staff.it.uts.edu.au/%7Edebenham/> John Debenham
University of Technology/Australia
 <http://www.mli.gmu.edu/people/michalski.html> Ryszard Michalski
George Mason University/USA
 

 <http://www2.cs.uh.edu/%7Eceick/> Christoph F. Eick
University of Houston/USA
 <http://www.diee.unica.it/it/personale/personale.php?idp=30> Fabio Roli
University of Cagliari/Italy
 

 <http://ng.irb.hr/cgi-bin/rimenik.cgi?keywords=gamberger&LANG=HR>
Dragan Gamberger
Rudjer Boskovic Institute/Croatia
 <http://www-biocib.cib.na.cnr.it/ml2/Gabry/gabry.html> Gabriella
Sanniti di Baja
Instituto di Cibernetica/Italy
 

 <http://www.diee.unica.it/%7Egiacinto/> Giorgio Giacinto
University of Cagliari/Italy
 <http://www.cs.washington.edu/homes/shapiro/> Linda Shapiro
University of Washington/USA
 

 <http://www.cs.uregina.ca/%7Ehamilton> Howard J. Hamilton
University of Regina/Canada
 <http://staff.science.uva.nl/%7Esmeulder/> Arnold Smeulders
University of Amsterdam/NL
 

 <http://mhjcc3-ei.eng.hokudai.ac.jp/web/makoto/hp/makoto-e.htm> Makato
Haraguchi
Hokkaido University Sapporo/Japan
 <http://www.ccs.neu.edu/home/pwang/> Patrick Wang
Northeastern University/USA
 

Atsushi Imiya
Chiba University/Japan
 <http://cs.gmu.edu/%7Ewechsler/> Harry Wechsler
George Mason University/USA
 

 <http://www.cs.cityu.edu.hk/people/academic> Horace Ip
City University/Hong Kong
 <http://www.research.ibm.com/dar/weiss-page.html> Sholom Weiss
IBM Yorktown Heights/USA
 

 <http://www.dlr.de/os/institut/mitarbeiter/hjahn> Herbert Jahn
Aero Space Center/Germany
 <http://www.usherbrooke.ca/informatique/personnel/profs/DZiou.html>
Djemel Ziou
Université de Sherbrooke/Canada
 

Aim of the Conference

The MLDM´2007 conference is the fifth event in a series of Machine
Learning and Data Mining meetings, initially organised as international
workshops. The aim of MLDM´2007 is to bring together from all over the
world researchers dealing with machine learning and data mining, in
order to discuss the recent status of the research in the field and to
direct its further developments.
Basic research papers as well as application papers are welcome. All
kinds of applications are welcome, but special preference will be given
to multimedia related applications, biomedical applications, and
webmining. Paper submissions should be related but not limited to any of
the following topics: 
 
*	association rules 
*	applications of clustering 
*	applications in medicine 
*	aspects of data mining 
*	autoamtic semantic annotation of media content 
*	Bayesian models and methods 
*	conceptional learning and clustering 
*	case-based reasoning and learning 
*	classification and interpretation of images, text, video 
*	classification and model estimation 
*	case-cased reasoning and associative memory 
*	content-based image retrieval 
*	decision trees 
*	deviation and novelty detection 
*	ensemble methods 
*	feature grouping, discretization, selection and transformation 
*	feature learning 
*	frequent pattern mining 
*	high-content analysis of microscopic images in medicine,
biotechnology and chemistry 
*	Goodness measures and evaluation (e.g., false discovery rates) 
*	inductive learning including decision tree and rule induction
learning 
*	knowledge extraction from text, video, signals and images 
*	learning/adaption of recognition and perception 
*	learning of internal representations and models 
*	learning of appropriate behaviour 
*	learning of action patterns 
*	learning in image pre-processing and segmentation 
*	learning and adaptive control 
*	learning robots 
*	learning in process automation 
*	learning for handwriting recognition 
*	learning of semantic inferencing rules 
*	learning of ontologies 
*	learning of visual ontologies 
*	mining gene data bases and biological data bases 
*	mining images, temporal-spatial data, images from remote sensing

*	mining text documents 
*	mining structural representations such as log files, text
documents and htm- documents 
*	mining financial or stockmarket data 
*	mining images in computer vision 
*	mining images and texture 
*	mining motion from sequence 
*	network analysis and intrusion detection 
*	neural methods 
*	nonlinear function learning and neural net based learning 
*	organisational learning and evolutional learning 
*	probabilistic information retrieval 
*	rule induction and grammars 
*	retrieval methods 
*	real-time event learning and detection 
*	Selection bias 
*	Sampling methods 
*	Selection with small samples 
*	similarity measures and learning of similarity 
*	statistical learning and neural net based learning 
*	support vector machines 
*	subspace methods 
*	statistical and conceptual clustering methods: basics 
*	statistical and evolutionary learning 
*	speech analysis 
*	symbolic learning and neural networks in document processing 
*	time series and sequential pattern mining 
*	text mining 
*	visualization and data mining 
*	video mining 
 
 

Invited Lecture

Data Clustering: User’s Dilemma
 <http://www.cse.msu.edu/%7Ejain/> Anil K. Jain
 
Department of Computer Science and Engineering, Michigan State
University (USA)

Important Dates


Deadline for paper submission:
January 9, 2007

Notification of acceptance:
March 22, 2007

Final paper submission:
April 10, 2007
 
Authors can submit their papers in long or short version:
 
Long Papers
Long papers must be formatted in the
<http://www.springeronline.com/sgw/cda/frontpage/0,10735,5-164-2-72376-0
,00.html> Springer LNCS format. They should have at most 15 pages.
Papers will be reviewed by the program committee. Accepted long papers
will appear in the proceedings book "Machine Learning and Data Mining in
Pattern Recognition" published by Springer Verlag in the LNAI series
. 
Short Papers
Short papers can be used to describe work in progress or project ideas.
They should have no more than 5 pages, formatted in
<http://www.springeronline.com/sgw/cda/frontpage/0,10735,5-164-2-72376-0
,00.html> Springer LNCS format. Accepted short papers will be presented
as posters in the poster session. They will be published in a special
poster proceedings book. 
 
Special Issue
Extended versions of selected papers will be published in a special
issue of an international journal after the conference.
 
 
 


More information about the Comp-bio mailing list

Send comments to us at biosci-help [At] net.bio.net