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!
Aims
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.
Participation [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):
• 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.
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
CAMDA
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.
ICDAR
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.
TREC
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.
ICPR
In conjunction with the International Conference on Pattern
Recognition,
ICPR 2004, a face recognition contest is being organized.
CASP
An important competition in protein structure prediction called
Critical
Assessment of
Techniques for Protein Structure Prediction.
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)
Help:
aal@ clopinet . com
INNS Special Interest Group
on Autonomous Machine Learning (SIG AML)
