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.

All kinds of applications are welcome but special preference will be given to multimedia related applications, biomedical applications, and webmining. MLDM´2007 is the 6st event in a series of MLDM events that have been originally started out as a workshop. Paper submissions should be related but not limited to any of the following topics:

* inductive learning including decision tree and rule induction learning
* conceptional learning and clustering
* case-based reasoning and learning
* similarity measures and learning of similarity
* association rules
* visualization and data mining
* video mining
* mining structural representations such as log files, text documents and HTML documents
* statistical learning and neural net based learning
* classification and interpretation of images, text, video
* organisational learning and evolutional learning
* probabilistic information retrieval
* mining gene data bases and biological data bases
* mining images, temporal-spatial data, images from remote sensing
* mining text documents
* knowledge extraction from text, video, signals and images
* Classification and Model Estimation
* Decision Trees
* Case-Based Reasoning and Associative Memory
* Rule Induction and Grammars
* Neural Methods
* Nonlinear Function Learning and Neural Net Based Learning
* Support Vector Machines
* Bayesian Models and Methods
* Subspace Methods
* Statistical and Conceptual Clustering Methods: Basics
* Applications of Clustering
* Feature Grouping, Discretization, Selection and Transformation
* Feature Learning
* Learning/adaption of recognition and perception
* Learning of internal representations and models
* Learning of appropriate behaviour
* Learning of action patterns
* Learning in Image Pre-Processing and Segmentation
* Statistical and Evolutionary Learning
* Retrieval Methods
* Content-Based Image Retrieval
* Applications in Medicine
* Time Series and Sequential Pattern Mining
* Mining Financial or Stockmarket Data
* Frequent Pattern Mining
* Mining Images in Computer Vision
* Mining Images and Texture
* Mining Motion from Sequence
* Real-Time Event Learning and Detection
* Speech Analysis
* Aspects of Data Mining
* Text Mining
* Symbolic Learning and Neural Networks in Document Processing
* Deviation and Novelty Detection
* Learning and adaptive control
* Learning robots
* Learning in process automation
* Learning for Handwriting Recognition
* Network Analysis and Intrusion Detection
* Autoamtic Semantic Annotation of Media Content
* Learning of Semantic Inferencing Rules
* Learning of Ontologies
* Learning of Visual Ontologies
* High-Content Analysis of Microscopic Images in Medicine, Biotechnology and Chemistry