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MLDM Call for Paper

The Aim of the Conference
Topics of the conference
Program Committee
Deadlines and Publications

The Aim of the Conference

The aim of the conference is to bring together researchers from all over the world who deal with machine learning and data mining in order to discuss the recent status of the research and to direct further developments. Basic research papers as well as application papers are welcome.

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Topics of the conference

All kinds of applications are welcome but special preference will be given to multimedia related applications, applications from live sciences 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
  • 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 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
  • Statistical and Evolutionary Learning
  • Subspace Methods
  • Support Vector Machines
  • Symbolic Learning and Neural Networks in Document Processing
  • Time Series and Sequential Pattern Mining
  • Audio Mining
  • Cognition and Computer Vision
  • Clustering
  • Classification & Prediction
  • Statistical Learning
  • Association Rules
  • Telecommunication
  • Design of Experiment
  • Strategy of Experimentation
  • Capability Indices
  • Deviation and Novelty Detection
  • Control Charts
  • Design of Experiments
  • Capability Indices
  • Conceptional Learning
  • Goodness Measures and Evaluation (e.g. false discovery rates)
  • Inductive Learning Including Decision Tree and Rule Induction Learning
  • Organisational Learning and Evolutional Learning
  • Sampling Methods
  • Similarity Measures and Learning of Similarity
  • Statistical Learning and Neural Net Based Learning
  • Visualization and Data Mining
  • Deviation and Novelty Detection
  • Feature Grouping, Discretization, Selection and Transformation
  • Feature Learning
  • Frequent Pattern Mining
  • Learning and Adaptive Control
  • Learning/Adaption of Recognition and Perception
  • Learning for Handwriting Recognition
  • Learning in Image Pre-Processing and Segmentation
  • Mining Financial or Stockmarket Data
  • Mining Motion from Sequence
  • Subspace Methods
  • Support Vector Machines
  • Time Series and Sequential Pattern Mining
  • Desirabilities
  • Graph Mining
  • Agent Data Mining
  • Applications in Software Testing


Authors can submit their paper in long or short version.

Long Paper

The paper must be formatted in the Springer LNCS format. They should have at most 15 pages. The papers will be reviewed by the program committee.

Short Paper

Short papers are also welcome and can be used to describe work in progress or project ideas. They can have 5 to max. 15 pages, formatted in Springer LNCS format. Accepted short papers will be presented as poster in the poster session. They will be published in a special poster proceedings book.

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Program Committee

Petra Perner IBaI, Germany
Piotr Artiemjew University of Warmia and Mazury in Olsztyn, Poland
Ming-Ching Chang University of Albany, USA
Robert Haralick City University of New York, USA
Chih-Chung Hsu National Cheng Kung University, Taiwan
Adam Krzyzak Concordia University, Canada
Krzysztof Pancerz Academy of Zamosc, Poland
M. Zakeria Kurdi University of Lynchburg, USA
Dan Simovici University of Massachusetts Boston, USA
Tanveer Syeda-Mahmood IBM Almaden Research Center, USA
Yi Wei Samsung Research America Inc., USA
Agnieszka Wosiak Lodz University of Technology, Poland

An industrial exhibition running in connection with the conference will give you the opportunity to look at new trends and systems in industry and to present your research to industry.

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