85 West St,

New York,

NY,

USA,

ZIP: 10006

Phone: +4917652567124

The MLDM conference this year is one of the conferences in a series of MLDM events that have been originally started out as a workshop in 1999.

Researcher and Industrial People from different fields will present theoretical aspects and their applications, and the results obtained by applying data mining. Besides that, newcomers in the field can get a fast introduction to Data Mining by taking the tutorial running in connection with the conference.

In a special industry session practicioners from different industrial branches can present their ongoing projects and discuss their work with the auditorium. An industrial exhibition, where companies are able to present their data-mining tools or applications including data-mining procedures will round up the conference.

The Problem and Solution hour will give you the opportunity to present your application and ask for support by researchers.

Meet up at the social event will give you the opportunity to compare notes with top leading researchers in Data Mining and Machine Learning from all over the world.

Target groups of MLDM
Researchers and PhD students
Researchers doing theoretical and applied research in data mining and machine learning in pattern recognition.

Practicioners
Practicioners from different industrial, social or economic branches interested in ground breaking developments in the field of machine learning and data mining.
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, 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 and 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
For further information please visit: www.mldm.de.

Tickets: https://go.evvnt.com/813228-2?pid=4528

 

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