Connectionists: "Concept Drift, Domain Adaptation & Learning in Dynamic Environments" SS @ IJCNN 2017

[Giacomo Boracchi]

CALL FOR PAPERS Special Session on
"Concept Drift, Domain Adaptation & Learning in Dynamic Environments"
will be held within the INNS-IEEE IJCNN 2017,
Anchorage Alaska in May, 14-19 2017.

http://home.deib.polimi.it/boracchi/events/ijcnn2017_SS/index.html
http://www.ijcnn.org/

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IMPORTANT DATES
Paper submission: November 15th, 2016
Paper Decision notification: January 20th, 2017
Camera-ready submission: February 20th, 2017
Conference Dates: May 14 - 19th, 2017
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One of the fundamental goals in computational intelligence is to achieve
brain-like intelligence, a remarkable property of which is the ability to
incrementally learn from noisy and incomplete data, and ability to adapt to
changing environments. The special session aims at presenting novel
approaches to incremental learning and adaptation to dynamic environments
both from the more traditional and theoretical perspective of computational
intelligence and from the more practical and application-oriented one.

This Special Session aspires at building a bridge between academic and
industrial research, providing a forum for researchers in this area to
exchange new ideas with each other, as well as with the rest of the neural
network & computational intelligence community.

*Topics*
Papers must present original work or review the state-of-the-art in the
following non-exhaustive list of topics:
. Methodologies/algorithms/techniques for learning in
dynamic/non-stationary environments
. Incremental learning, lifelong learning, cumulative learning
. Domain adaptation and covariate-shift adaptation
. Semi-supervised learning methods for nonstationary environments
. Ensemble methods for learning in nonstationary environments
. Learning under concept drift and class imbalance
. Learning recurrent concepts
. Change-detection and anomaly-detection algorithms
. Information-mining algorithms in nonstationary datastreams
. Cognitive-inspired approaches for adaptation and learning
. Applications that call for learning in dynamic/non-stationary
environments, or change/anomaly detection, such as
o adaptive classifiers for concept drift
o adaptive/Intelligent systems
o fraud detection
o fault detection and diagnosis
o network-intrusion detection and security
o intelligent sensor networks
o time series analysis
. Benchmarks/standards for evaluating algorithms learning in
non-stationary/dynamic environments

*Keywords*
Concept drift, nonstationary environment, change/anomaly detection, domain
adaptation, incremental learning, data streams.


*Paper Submission*
THE DEADLINE FOR THE PAPER SUBMISSION TO THE SPECIAL SESSION IS THE SAME OF
IJCNN 2017, November 15th 2017.

All the submissions will be peer-reviewed with the same criteria used for
other contributed papers.

Perspective authors will submit their papers through the IJCNN 2017
conference submission system at http://www.ijcnn.org/
Please make sure to select the Special Session nr 5 "Concept Drift, Domain
Adaptation & Learning in Dynamic Environments" from the "S. SPECIAL SESSION
TOPICS" name in the "Main Research topic" dropdown list;

Templates and instruction for authors will be provided on the IJCNN webpage
http://www.ijcnn.org/

All papers submitted to the special sessions will be subject to the same
peer-review procedure as regular papers, accepted papers will be published
in the IEEE Conference Proceedings .

Further information about IJCNN 2017 can be found at http://www.ijcnn.org/

For any question you may have about the Special Session or paper
submission, feel free to contact Giacomo Boracchi

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Special Session on
"Concept Drift, Domain Adaptation & Learning in Dynamic Environments" @
IEEE IJCNN 2017

*Organizes*
. Giacomo Boracchi (Politecnico di Milano, Dipartimento di Elettronica,
Informazione e Bioingegneria, Italy)
. Robi Polikar (Rowan University, Glassboro, NJ, USA)
. Manuel Roveri (Politecnico di Milano, Dipartimento di Elettronica,
Informazione e Bioingegneria, Italy)
. Gregory Ditzler, (University of Arizona, AZ, USA)

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