Call for Papers
16th International Conference on Machine Learning and Data Mining
MLDM´2020
www.mldm.de
July 18 - 23, 2020
New York, USA

Chair:
Petra Perner
Institute of Computer Vision and applied Computer Sciences, IbaI,
Leipzig, Germany

Program Committee
Reneta Barneva The State University of New York at Fredonia, USA
Michelangelo Ceci Universtiy of Bari, Italy
Ireneusz Czarnowski Gdynia Maritime University, Poland
Roberto Corrizo Universtiy of Bari, Italy
Christoph F. Eick Universtiy of Houston, USA
Mark J. Embrechts Rensselaer Polytechnic Institute and CardioMag Imaging, Inc, USA
Ana Fred Technical University of Lisboa, Portugal
Giorgio Giacinto University of Cagliari, Italy
Aminata Kane Concordia University, Canada
Piet Kommers University of Twente, The Netherlands
Olga Krasotkina Russian Stae University, Russia
Dimitris Karras Chalkis Institute of Technology, Greece
Adam Krzyzak Concordia University, Canada
Valerio Pascucci University of Utah, USA
Gianvito Pio University of Bari, Italy
Francis E.H. Tay National University of Singapore, Singapore
Turki Turki King Abdulaziz University, Saudi Arabia
Zeev Volkovich ORT Braude College of Engineering, Israel
Patrick Wang Northeastern University, USA

Aim of the Conference The MLDM´2018 conference is the fourteenth event in a series of Machine Learning and Data Mining meetings. The aim of MLDM 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:

• similarity measures and learning * 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 * 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, 2020
Notification of acceptance: March 18, 2020
Submission of camera-ready copy: April 05, 2020

Authors can submit their papers in long or short version: Please submit the electronic version of your camera-ready paper through the conference management system (http://www.easychair.org/CMS/). If you have any problems with the system please do not hesitate to contact info@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.

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 Medicine, 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 2020
* Intern. Workshop on Data Mining in Marketing DMM 2020
* Intern. Workshop Case-Based Reasoning CBR-MD-AI&PR 2020
* Intern. Workshop on Multimedia Forensic Data Analysis Forensic 2020

Exhibition 19th Industrial Exhibition on Intelligent Data and Image Analysis IEDA 2020 We like to invite you to present your company or publishing house at the Industrial Exhibition ieda 2020 (www.iedaexhibition.de).