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*3rd Call for Papers *

13th International Conference on Machine Learning and Data Mining in
Pattern Recognition  (*MLDM´2017*)

           www.mldm.de

July 15th - 21st, 2017

 New York, USA

Chair: Prof. Dr. Petra Perner

     Institute of Computer Vision and applied Computer Sciences, IBaI
Leipzig/Germany

Program Committee

Sergey Ablameyko    Belarus State University, Belarus

Michelangelo Ceci    Universtiy of Bari, Italy

Patrick Bouthemy    INRIA VISTA, France

Xiaoqing Ding        Tsinghua University, China

Christoph F. Eick    Universtiy of Houston, USA

Ana Fred                   Technical University of Lisboa, Portugal

Giorgio Giacinto    University of Cagliari, Italy

Makato Haraguchi    Hokkaido University of Sapporo, Japan

Dimitris Karras        Chalkis Institute of Technology, Greece

Adam Krzyzak        Concordia University, Canada

Thang V. Pham        University of Amsterdam, The Netherlands

Linda Shapiro        University of Washington, USA

Shashank Shekhar    VMware Inc., USA

Tamas Sziranyi        MTA-SZTAKI, Hungary

Francis E.H. Tay    National University of Singapore, Singapore

Alexander Ulanov    HP Labs, Russia

Zeev Volkovich        ORT Braude College of Engineering, Israel

Patrick Wang        Northeastern University, USA

Aim of the Conference

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

The MLDM´2015 conference is the tenth event in a series of Machine Learning
and Data Mining meetings,

initially organised as international workshops. The aim of MLDM´2015 is to
bring together from all over

the world researchers dealing with machine learning and data mining, in
order to discuss the recent

status of the research in the field and to direct its further developments.

Basic research papers as well as application papers are welcome. All kinds
of applications are welcome,

but special preference will be given to multimedia related applications,
biomedical applications, and

webmining. Paper submissions should be related but not limited to any of
the following topics:

*    association rules

*    case-based reasoning and learning

*    classification and interpretation of images, text, video

*    conceptional learning and clustering

*    Goodness measures and evaluaion (e.g. false discovery rates)

*    inductive learning including decision tree and rule induction learning

*    knowledge extraction from text, video, signals and images

*    mining gene data bases and biological data bases

*    mining images, temporal-spatial data, images from remote sensing

*    mining structural representations such as log files, text documents
and HTML documents

*    mining text documents

*    organisational learning and evolutional learning

*    probabilistic information retrieval

*    Selection bias

*    Sampling methods

*    Selection with small samples

*    similarity measures and learning of similarity

*    statistical learning and neural net based learning

*    video mining

*    visualization and data mining

*    Applications of Clustering

*    Aspects of Data Mining

*    Applications in Medicine

*    Autoamtic Semantic Annotation of Media Content

*    Bayesian Models and Methods

*    Case-Based Reasoning and Associative Memory

*    Classification and Model Estimation

*    Content-Based Image Retrieval

*    Decision Trees

*    Deviation and Novelty Detection

*    Feature Grouping, Discretization, Selection and Transformation

*    Feature Learning

*    Frequent Pattern Mining

*    High-Content Analysis of Microscopic Images in Medicine, Biotechnology
and Chemistry

*    Learning and adaptive control

*    Learning/adaption of recognition and perception

*    Learning for Handwriting Recognition

*    Learning in Image Pre-Processing and Segmentation

*    Learning in process automation

*    Learning of internal representations and models

*    Learning of appropriate behaviour

*    Learning of action patterns

*    Learning of Ontologies

*    Learning of Semantic Inferencing Rules

*    Learning of Visual Ontologies

*    Learning for robots

*    Mining Images in Computer Vision

*    Mining Images and Texture

*    Mining Motion from Sequence

*    Neural Methods

*    Network Analysis and Intrusion Detection

*    Nonlinear Function Learning and Neural Net Based Learning

*    Real-Time Event Learning and Detection

*    Retrieval Methods

*    Rule Induction and Grammars

*    Speech Analysis

*    Statistical and Conceptual Clustering Methods: Basics

*    Statistical and Evolutionary Learning

*    Subspace Methods

*    Support Vector Machines

*    Symbolic Learning and Neural Networks in Document Processing

*    Time Series and Sequential Pattern Mining

*    Mining Social Media

*    Audio Mining

*    Cognition and Computer Vision


Important Dates

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

Deadline for paper submission:        January 15, 2017

Notification of acceptance:        March 18 , 2017

Submission of camera-ready copy:    April 05, 2017

Submission guidelines

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

Authors can submit their papers in long or short version:

Please submit the electronic version of your camera-ready paper through the
COMMENCE

conference management system (http://www.mldm.de/CMS/). If you have any
problems with

the system please do not hesitate to contact i...@mldm.de.

Long Papers

Long papers must be formatted in the Springer LNCS format. They should have
at most 15 pages. Papers will be reviewed by the program committee.
Accepted long papers will appear in the proceedings book "Machine Learning
and Data Mining in Pattern Recognition" published by Springer Verlag in the
LNAI series.  Extended versions of selected papers will be published in a
special issue of an international journal after the conference.

Short Papers

Short papers are also welcome and can be used to describe work in progress
or project ideas. They should have no more than 5 pages and must be also
formatted in Springer LNCS format. Accepted short papers will be presented
as posters in the poster session.

They will be published in a special poster proceedings book. Papers will be
submitted via

the online reviewing system.

Best Paper Award

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

The best scientific paper is distinguished with a Best Paper Award. A
outstanding contribution in the field of Machine Learning and Data Mining
for Pattern Recognition will be awarded with the Best Paper Award.

There will be three nominations for the Best Paper Award before the
conference. All nominee will receive

a certificate. The final decision of the Best Paper Award will be made
based on the presentation and

the discussion.

The Best Paper Award will be a certificate and a prize money of 500 Euro
for the research team.


Tutorials

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

Data Mining Tutorial

Prof. Dr. Petra Perner, Institute of Computer Vision and Applied Computer
Sciences IBaI,

http://www.data-mining-forum.de/t_dm.php

Case-Based Reasoning Tutorial

Prof. Dr. Petra Perner, Institute of Computer Vision and Applied Computer
Sciences IBaI,

http://www.data-mining-forum.de/t_cbr.php

Intelligent Image Interpreation and Computer Vision in Mediceine,
Biotechnology, Chemistry & Food Industry

Prof. Dr. Petra Perner, Institute of Computer Vision and Applied Computer
Sciences IBaI,

http://www.data-mining-forum.de/t_iicv.php


Workshops (http://www.data-mining-forum.de/workshops.php)

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

   * Intern. Workshop I-Business to Manufacturing and LifeScience B2ML 2017

   * Intern. Workshop on Data Mining in Marketing DMM 2017

   * Intern. Workshop Case-Based Reasoning CBR-MD 2017

   * Intern. Workshop on Multimedia Forensic Data Analysis MFDA 2017

Exhibition

-----------

16th Industrial Exhibition on Intelligent Data and Image Analysis IEDA 2017

We like to invite you to present your company or publishing house at the
Industrial Exhibition ieda 2017

(www.iedaexhibition.de).


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-- 
Regards,
Sandhya
------------
Postdoctoral Research Scientist,
Department of Biological Sciences,
Columbia University,
New York City - 10027.
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