-------- Weitergeleitete Nachricht -------- Betreff: [WI] ECML Workshop on Advanced Analytics and Learning on Temporal Data Datum: Fri, 12 Jun 2015 09:22:20 +0200 Von: F. Höppner f.hoeppner@ostfalia.de An: wi@lists.uni-karlsruhe.de
[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