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´2011 is the 7th 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:

* association rules
* Audio Mining
* 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 robots
* Mining Financial or Stockmarket Data
* 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
* Text Mining
* Time Series and Sequential Pattern Mining