Hi, ** We apologize in advance if you receive multiple copies of this CFP ** ** Kindly help to distribute this CFP to your mailing list ** *4th 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´2017 conference is the tenth event in a series of Machine Learning and Data Mining meetings, initially organised as international workshops. The aim of MLDM´2017 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 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. 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). If you do not with to receive the newsletter, please press here http://ibai-institut-newsletter.de/cgi-bin/mail/manager.cgi? action=delete&email=pperner%40ibai-institut.de&group1=cbr.