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ECML/PKDD 2015 : ECML'2015 Workshop on Advanced Analytics and Learning on Temporal Data

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Link: http://ama.liglab.fr/aaltd_ecml2015/
 
When Sep 11, 2015 - Sep 11, 2015
Where Porto
Submission Deadline Jun 22, 2015
Notification Due Jul 13, 2015
Final Version Due Jul 27, 2015
 

Call For Papers

ECML/PKDD 2015 Workshop on

Advanced Analytics and Learning on Temporal Data

CALL FOR PAPERS

http://ama.liglab.fr/aaltd_ecml2015/

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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. 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 in PDF or PostScript using the LNCS formatting style, 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 Höeppner, 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

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