MLDM 2012 - 8th International Conference on Machine Learning and Data Mining MLDM 2012

Website www.mldm.de | Edit Freely

Category MLDM 2012

Deadline: December 18, 2011 | Date: July 13, 2012-July 20, 2012

Venue/Country: Berlin, Germany

Updated: 2011-11-02 20:39:45 (GMT+9)

Call For Papers - CFP

Machine Learning and Data Mining MLDM

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´2012 is the 8th 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

Mining Social Media