Chia LagunaDon RubinBurr SettlesEugene TuvMing Hen TsaiJianjun XieChris LovellChristian GrozeaOlivier PietquinZalan BodoAISTATS
Souvenir pictures:
From top to bottom and left to right: Chia Laguna, Don Rubin, Burr Settles, Eugene Tuv, Ming Hen Tsai and Isabelle Guyon, Jianjun Xie, Chris Lovell, Christian Grozea, Olivier Pietquin, Zalan Bodo, and the audience!


This special session aims to offer a meeting opportunity for academics and industry-related researchers, belonging to the various communities of Computational Intelligence, Machine Learning, Vision systems, Experimental Design and Data Mining to discuss new areas of active and autonomous learning, and to bridge the gap between data acquisition or experimentation and model building. How active sampling and data acquisition, can contribute towards the design and modelling of highly intelligent autonomous learning systems for the recognisition of complex (abnormal) behaviours is intended to be the catalyst and the aggregation stimulus for the overall event. Thus, there is a considerable interest in developing new methods and in extending and adapting existing traditional approaches. The results of the active learning competition will also be discussed in this special session.

[CFP in PDF format]

Potential participants are invited to submit a paper to the special session on Active an Autonomous Learning. Please follow the regular submission guidelines of WCCI 2010 and submit your paper to the paper submission system. IMPORTANT: Select the correct special session: S111 Active and Autonomous Learning (AAL) at the bottom of the "Main Research Topic" menu AND notify the chairs of your submission by sending email to: aal@ clopinet . com.

Topics of interest to the workshop include (but are not limited to):

• Experimental Design
• Active Learning
• Autonomous Learning
• Incremental Learning
• Autonomous intelligent systems
• On-line learning
• Machine Learning for Data Mining
• Learning from unlabeled data.
• Artificial Vision
• Agent and Multi-Agent Systems
• Hybrid Systems
• Unsupervised Learning
• Classification Methods
• Novelty Detection
• Surveillance Systems and solutions (object tracking, multi-camera algorithms,
behaviour analysis and learning, scene segmentation, system architecture
aspects, operational procedures, usability, scalability) 
• Gesture and Posture Analysis and Recognition 
• Case Studies (shopping malls, railway stations, airport lounges, bank branches, etc) 
• Autonomous Robots
• Industrial and Commercial Applications of Intelligent Methods
• Biometric Identification and Recognition
• Extraction of Biometric Features (fingerprint, iris, face, voice, palm, gait)
• Email, Web and Networks Security

Participation in the active learning competition is not required to attend the workshop and vice versa.
Competition participants can apply for travel support.


WCCI 2010, Barcelona, Spain, July 19-23, 2010

The  schedule  of the special session is to be announced.

Important dates:
Paper submission deadline: 31 January 2010    7 February 2010
Competition final testing period begins: 1 February 2010
Competition ends: 28 February 2010

Notification of acceptance: 15 March 2010
Camera-ready: 02 May 2010
Early registration: 23 May 2010

Links to related workshops/competitions

NIPS 2009 causality and time series mini-symposium.  Featuring a memorial lecture of Clive Granger by Halbert White.

NIPS 2008 causality workshop: objectives and assessment. The second challenge in causality organized by the causality workbench.

WCCI  2008 causation and prediction challenge. A first activity of the causality workbench.

NIPS 2006 workshop on causality and feature selection. The ancestor of this workshop.

IJCNN 2007 Agnostic learning vs. Prior knowledge challenge. “When everything fails, ask for additional domain knowledge” is the current motto of machine learning. Therefore, assessing the real added value of prior/domain knowledge is a both deep and practical question.The participants competed in two track: the “prior knowledge track” for which they had access to the raw data and information about the data, and the “agnostic learning track” for which they had access to preprocessed data with no knowledge of the identity of the features.

WCCI 2006 performance prediction challenge. “How good are you at predicting how good you are? 145 participants tried to answer that question. Cross-validation came very strong. Can you do better? Measure yourself against the winners by participating to the model selection game.

NIPS 2003 workshop on feature extraction and feature selection challenge. We organized a competition on five data sets in which hundreds of entries were made. The web site of the challenge is still available for post challenge submissions. Measure yourself against the winners! See the book we published with a CD containing the datasets, tutorials, papers on s.o.a. methods.

Pascal challenges: The Pascal network is sponsoring several challenges in Machine learning.

Data mining competitions:
A list of data mining competitions maintained by KDnuggets, including the well known KDD cup.

List of data sets for machine learning:
A rather comprehensive list maintained by MLnet.

UCI machine learning repository: A great collection of datasets for machine learning research.

DELVE: A platform developed at University of Torontoto benchmark machine learning algorithms.

Critical Assessment of Microarray Data Analysis, an annual conference on gene expression microarray data analysis. This conference includes a context with emphasis on gene selection, a special case of feature selection.

International Conference on Document Analysis and Recognition, a bi-annual conference proposing a contest in printed text recognition. Feature extraction/selection is a key component to win such a contest.

Text Retrieval conference, organized every year by NIST. The conference is organized around the result of a competition. Past winners have had to address feature extraction/selection effectively.

In conjunction with the International Conference on Pattern Recognition, ICPR 2004, a face recognition contest is being organized.

An important competition in protein structure prediction called Critical Assessment of
 Techniques for Protein Structure Prediction.

Contact information

Session chairs:
José García-Rodríguez
(University of Alicante, Spain)
Isabelle Guyon
(Clopinet Enterprises, USA)
Anastassia Angelopoulou
(University of Westminster, UK)
Vincent Lemaire
(Orange, France) 

Organizing committee:
Matthias Adankon (Ecole de technologie supérieure de Montréal, Canada)
Jorge Azorín (University of Alicante, Spain)
Alexis Bondu (EDF, France)
Marc Boullé
(Orange, France)
Gavin Cawley
(University of East Anglia, UK)
Olivier Chapelle
(Yahhoo!, California)
Emilio Corchado
(University of Burgos, Spain)
Juan Manuel Corchado
(University of Salamanca, Spain)
Gideon Dror
(Academic College of Tel-Aviv-Yaffo, Israel)
Emmanuel Faure
(Institut des systèmes complexes, Paris, France)
Shaogang Gong
(Queen Mary, University of London, UK)
Seiichi Ozawa (Kobe University, Japan)

Alexandra Psarrou
(University of Westminster, UK)
Peter Roth
(Graz University, Austria)
Asim Roy (Arizona State University, USA)
Amir Reza Saffari Azar (Graz University of Technology)
Fabrizio Smeraldi
(Queen Mary, University of London, UK)
Alexander Statnikov
(New York University, USA)


aal@ clopinet . com


INNS Special Interest Group on Autonomous Machine Learning (SIG AML)

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