-------- Weitergeleitete Nachricht --------
[Apologies for cross-postings]
################################################################
*ECML/PKDD 2015 Workshop on*
*Advanced Analytics and Learning on Temporal Data*
*CALL FOR PAPERS*
http://ama.liglab.fr/aaltd_ecml2015/
#################################################################
2015 International Workshop on Advanced Analytics and Learning on
Temporal Data (AALTD 2015) will be held Friday, September 11, 2015 in
Porto, Portugal, co-located with ECML/PKDD 2015
<http://ecmlpkdd2015.org/>. The aim of this workshop is to bring
together researchers and experts in machine learning, data mining,
pattern analysis and statistics to share their challenging issues and
advance researches on temporal data analysis. Analysis and learning from
temporal data cover a wide scope of tasks including learning metrics,
learning representations, unsupervised feature extraction, clustering
and classification.
Temporal data are frequently encountered in a wide range of domains such
as bio-informatics, medicine, finance and engineering, among many
others. They are naturally present in applications covering language,
motion and vision analysis, or more emerging ones as energy efficient
building, smart cities, dynamic social media or sensor networks.
Contrary to static data, temporal data are of complex nature, they are
generally noisy, of high dimensionality, they may be non stationary
(i.e. first order statistics vary with time) and irregular (involving
several time granularities), they may have several invariant
domain-dependent factors as time delay, translation, scale or tendency
effects. These temporal peculiarities make limited the majority of
standard statistical models and machine learning approaches, that mainly
assume i.i.d data, homoscedasticity, normality of residuals, etc. To
tackle such challenging temporal data, one appeals for new advanced
approaches at the bridge of statistics, time series analysis, signal
processing and machine learning. Defining new approaches that transcend
boundaries between several domains to extract valuable information from
temporal data is undeniably a hot topic in the near future, that has
been yet the subject of active research this last decade.
Topics of Interest
The proposed workshop welcomes papers that cover, but not limited to,
one or several of the following topics:
* Temporal data clustering
* Semi-supervised and supervised classification on temporal data
* Deep learning and learning representations for temporal data
* Metric and kernel learning for temporal data
* Modeling temporal dependencies
* Advanced forecasting and prediction models
* Space-temporal statistical analysis
* Functional data analysis methods
* Temporal data streams
* Dimensionality reduction, sparsity, algorithmic complexity and big
data challenge
* Bio-informatics, medical, energy consumption, applications on
temporal data
* Benchmarking and assessment methods for temporal data
We also encourage submissions which relate research results from other
areas to the workshop topics.
*Submission of Papers*
Please send to Ahlame Douzal <mailto:Ahlame.Douzal@imag.fr> in PDF or
PostScript using the LNCS formatting style
<http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0>, a short
paper from 2 to 6 pages, or an extended abstract of less than 2000 words
for one of the two tracks:
* *Oral presentation*
* *Poster session *(including research in progress and demos).
It will be considered to invite authors of selected papers for
publication in a special volume in the Lecture Notes in Computer Science
(LNCS) series.
*Important Dates*
Workshop paper submission deadline: * June 22, 2015*
Workshop paper acceptance notification:
July 13, 2015
Workshop paper camera-ready deadline:
July 27, 2015
Workshop date:
September 11, 2015
*Organizers*
* Ahlame Douzal-Chouakria, Université Grenoble Alpes, France
* José Antonio Vilar Fernández, University of A Coruña, Spain
* Pierre-François Marteau, IRISA, Université de Bretagne-Sud, France
* Ann Maharaj, Monash University, Australia
* Andrés Modesto Alonso Fernandez, Universidad Carlos III de Madrid,
Spain
* Edoardo Otranto, University of Messina, Italy
*Reviewing Committee*
* Massih-Reza Amini, Université Grenoble Alpes, France
* Manuele Bicego, University of Verona, Italy
* Gianluca Bontempi, MLG, ULB University, Belgium
* Antoine Cornuéjols, LRI, AgroParisTech, France
* Pierpaolo D'Urso, University La Sapienza, Italy
* Patrick Gallinari, LIP6, UPMC, France
* Eric Gaussier, Université Grenoble Alpes, France
* Christian Hennig, Department of Statistical Science, London's Global
Univ, UK
* Frank Höppner, Ostfalia University of Applied Sciences, Germany
* Paul Honeine, ICD, Université de Troyes, France
* Vincent Lemaire, Orange Lab, France
* Manuel Garcia Magarinos, University of A Coruña, Spain
* Mohamed Nadif, LIPADE, Université Paris Descartes, France
* François Petitjean, Monash University, Australia
* Fabrice Rossi, SAMM, Université Paris 1, France
* Allan Tucker, Brunel University, UK
--
Mailing-Liste: wi@lists.kit.edu
Administrator: wi-request@lists.kit.edu
Konfiguration: https://www.lists.kit.edu/wws/info/wi