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CLASS-L  September 2011

CLASS-L September 2011

Subject:

Call for Papers Special Sessions ICPRAM 2012

From:

Pedro Latorre Carmona <[log in to unmask]>

Reply-To:

Classification, clustering, and phylogeny estimation

Date:

Thu, 15 Sep 2011 09:14:48 +0200

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (280 lines)

Dear "Class-L" members,

My name is Pedro Latorre Carmona, program co-chair of the 2012  
"International Conference on Pattern Recognition Applications and  
Methods" (ICPRAM 2012) http://www.icpram.org

Please let me send you below the text version of the "Open Call for  
papers for Special Sessions" in the framework of this conference  
(deadline: October 24, 2011), which contains information about each  
Special Session, including the title, (co)chair(s), summary, etc.

I would like to let you all know that papers accepted in a Special  
Session have the same "rights" and follow the same rules as those of  
"regular" type, i. e., they will appear in the conference proceedings,  
will be elegible for the conference best paper prize and could be  
selected to be part of the two Special Issues that ICPRAM 2012 has.

Thanks!

Pedro Latorre Carmona.

*********************************************************************
       2012 International Conference on Pattern Recognition
                 Applications and Methods (ICPRAM2012)

                           CALL FOR PAPERS
                           SPECIAL SESSIONS

                          February 6-8, 2012
                     Vilamoura, Algarve, Portugal
                        http://www.icpram.org
*********************************************************************

Let me kindly inform you that there is an open call for papers, until
October 24, for the following Special Sessions:

- Algebraic Geometry in Machine Learning
Chair:
- Jason Morton, Pennsylvania State University, U.S.A.
http://icpram.org/AGML.asp


- Shape Analysis and Deformable Modeling
Chair:
- Xianghua Xie, Swansea University, U.K.
http://icpram.org/SADM.asp


- Machine Learning for Sequences
Chair:
- Thierry Artieres, Universite Pierre et Marie Curie (UPMC), France
http://icpram.org/MLS.asp


- Pattern Recognition Applications in Remotely Sensed Hyperspectral
Image Analysis
Chair:
- Antonio Plaza, University of Extremadura, Spain
http://icpram.org/PRARSHIA.asp


- High-Dimensional Inference from Limited Data: Sparsity, Parsimony
and Adaptivity
Co-chairs:
- Jarvis Haupt, University of Minnesota, U.S.A.
- Rui M. Castro, Eindhoven University of Technology, The Netherlands
http://icpram.org/HDILD.asp


- Interactive and Adaptive Techniques for Machine Learning,
Recognition and Perception
Co-chairs:
- Luisa Mico, University of Alicante, Spain
- Francesc J. Ferri, University of Valencia, Spain
http://icpram.org/IATMLRP.asp

These special sessions are part of the International Conference on
Pattern Recognition Applications and Methods (ICPRAM 2012 -
http://www.icpram.org), which is sponsored by the Institute for
Systems and Technologies of Information, Control and Communication
(INSTICC) in cooperation with the Association for the Advancement of
Artificial Intelligence (AAAI) and Pattern Analysis, Statistical
Modelling and Computational Learning (PASCAL2), and technically co-
sponsored by IEEE Signal Processing Society, Machine Learning for
Signal Processing (MLSP) Technical Committee of IEEE, AERFAI
(Asociacion Espanola de Reconocimiento de Formas y Analisis de
Imagenes) and APRP (Associacao Portuguesa de Reconhecimento de
Padroes). INSTICC is member of the Workflow Management Coalition
(WfMC).

ICPRAM will be held in Vilamoura, Algarve, Portugal next year, on
February 6-8, 2012.

IMPORTANT DATES:
Paper Submission: October 24, 2011
Authors Notification: November 11, 2011
Final Paper Submission and Registration: November 25, 2011


1) Algebraic Geometry in Machine Learning
Chair:
- Jason Morton, Pennsylvania State University, U.S.A.

Scope
The philosophy of algebraic statistics is that for many models arising
in statistics and machine learning, the space of parameters or
probability distributions modeled has the structure of an algebraic
variety. This observation has led to new precise characterizations of
popular models, new insights into representational power, and new
approaches to studying learning performance (e.g. in the neighborhood
of singularities, or proving the existence of a MLE).

For many classes of machine learning models, theoretical understanding
has lagged behind experimental success. In many cases,
representational power and performance characteristics are poorly
understood, and even proponents are unsure why they work.
Understanding the algebraic, polyhedral, and tropical geometry of
graphical models and other popular models has provided a new set of
tools enabling researchers to settle several open questions about
their capabilities, and progress on this front is expected to
continue.

Topics for the special session may include the algebraic geometry and
representation theory of machine learning models, the polyhedral and
tropical geometry of the space of functions they can compute,
geometric characterizations of architecture choice and asymptotic
performance, and related topics.


2) Shape Analysis and Deformable Modeling
Chair:
- Xianghua Xie, Swansea University, U.K.

Scope and Topics of interest:
Deformable modeling is a powerful tool in extracting object shape,
structure, and motion patterns. It is particularly suitable for non-
rigid objects and has been widely used to measure and model, for
instance, biological shape and shape evolution in medical data where
shape extraction and analysis have shown enormous promise in
understanding biological function and disease progression. Its
application has a wide reach in all areas of computer vision.

This special session is devoted to the discussion of recent advances
in shape analysis and deformable modeling, in particular, for non
rigid objects. Contributions presenting recent work on shape
representation, extraction, learning, classification and dynamic
modeling are particularly welcome.

The technical topics include, but are not limited to:
* Shape Representation and Learning
* Shape Matching, Classification, and Registration
* Active Shape Model and Active Appearance Model
* Active Contour and Surface Model
* Partial Differential Equations
* Level Set Methods
* Variational Methods
* Shape Based Motion Analysis
* Applications



3) Machine Learning for Sequences
Chair:
- Thierry Artieres, Universite Pierre et Marie Curie (UPMC), France

Scope
Sequence classification and sequence labeling is at the heart of many
pattern recognition and data mining tasks, in fields such as speech
and handwriting recognition, bioinformatics, etc. Beyond well known
Hidden Markov Models (HMMs), which have been widely used for modeling
sequences of patterns, a number of alternative methods and models have
been proposed in the recent years. These approaches include for
instance discriminative training (e.g. large margin) of Hidden Markov
Models, on-line learning of such models, discriminative models based
on Conditional Random Fields, etc.
This special session aims at sharing new ideas and works on models and
approaches for improving over state of the art methods for signal and
sequence classification and labeling tasks.



4) Pattern Recognition Applications in Remotely Sensed Hyperspectral
Image Analysis
Chair:
- Antonio Plaza, University of Extremadura, Spain

Scope
Hyperspectral imaging is concerned with the measurement, analysis and
interpretation of spectra acquired from a given scene by an airborne
or satellite imaging spectrometer providing information in narrow
wavelengths.
The special characteristics of remotely sensed hyperspectral images
pose different processing problems which must be necessarily tackled
under specific mathematical formalisms, such as classification and
segmentation, or spectral unmixing. For instance, several machine
learning techniques are now actively being applied to extract relevant
information (in supervised, semi-supervised or unsupervised fashion)
from remotely sensed hyperspectral data. This special session aims at
providing an overview of recent advances in the use of pattern
recognition and machine learning techniques for hyperspectral data
interpretation, with particular attention to specific aspects of
hyperspectral image analysis such as the presence of mixed pixels or
the high computational requirements introduced by the processing of
data sets provided by the latest generation of imaging instruments.



5) High-Dimensional Inference from Limited Data: Sparsity, Parsimony
and Adaptivity
Co-chairs:
- Jarvis Haupt, University of Minnesota, U.S.A.
- Rui M. Castro, Eindhoven University of Technology, The Netherlands

Scope and Topics
In recent years the signal processing and statistics communities have
witnessed a flurry of research activity aimed at the development of
new non- traditional sampling, sensing and inference methods, fueled
by the growing need to understand highly complex processes from
limited amounts of data.
For example, recent breakthrough results in compressive sampling have
shed new light on our understanding of sampling and reconstruction,
leading to revolutionary new technologies in a variety of application
domains, including RF communications and surveillance, imaging, and
genomics.

The enabling feature of this new wave of research is the notion that,
in many practical applications, high-dimensional objects of interest
possess some form of parsimonious or low-dimensional representation.
Identifying these representations and designing strategies for
effectively exploiting them comprises the central unifying theme of
many active research directions, including compressive and adaptive
sensing, matrix completion, and dictionary learning.

This session is devoted to the presentation and discussion of recent
advances in these broadly defined areas. Namely, we invite submissions
of high quality contributions in theory, methods, and/or applications
in the general area of high-dimensional inference from limited data.
Specific topics of interest for this session include (but are not
limited to):

     * Compressed Sensing
     * Active Learning and Adaptive Sensing
     * Sequential Experimental Design
     * Dictionary Learning
     * Optimal Information Gathering
     * Matrix Completion Approaches



6) Interactive and Adaptive Techniques for Machine Learning,
Recognition and Perception
Co-chairs:
- Luisa Mico, University of Alicante, Spain
- Francesc J. Ferri, University of Valencia, Spain

Scope
Human interaction is a very active field that is receiving increasing
attention in the pattern recognition and machine learning community.
In this new paradigm the systems do not perform only in an automatic
way but also in an interactive fashion.
The main reason for this is that automatic systems are not free from
errors and, being high quality results the principal objective, a kind
of supervision is needed. On the other hand, as time goes by,
intrinsic interactive applications are more important and frequent.The
use of the interactive paradigm in Pattern Recognition opens the door
to new challenges in order to make convenient use of a number of
emerging methods for supporting learning and data analysis in dynamics
contexts: active and adaptive learning, hypothesis generation, data
managed techniques, combining classifier techniques, probabilistic
learning, interactive transduction, etc. Moreover, another challenge
is the application of these ideas to interesting real-word tasks, as
human behavior analysis, text transcription, content-based image
retrieval, handwriting recognition, surveillance, biometric systems
and many others.
This special session welcomes articles on advances on all the
aforementioned hot topics.

----------------------------------------------
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