Difference between revisions of "MLDM 2009"

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  | Series =  
 
  | Series =  
 
  | Field = Machine learning
 
  | Field = Machine learning
  | Homepage = www.mldm.de/frame.htm
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  | Homepage = www.mldm.de
 
  | Start date = Jul 23, 2009  
 
  | Start date = Jul 23, 2009  
 
  | End date =  Jul 25, 2009
 
  | End date =  Jul 25, 2009
  | City= Leipzig Germany
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  | City= Leipzig
 
  | State =  
 
  | State =  
  | Country = Leipzig Germany
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  | Country = Germany
 
  | Abstract deadline =  
 
  | Abstract deadline =  
 
  | Submission deadline = Jan 6, 2009
 
  | Submission deadline = Jan 6, 2009
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<pre>
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=6th International Conference on Machine Learning and Data Mining=
The Aim of the Conference
+
==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.
 
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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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:
 
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
+
* inductive learning including decision tree and rule induction learning
    * conceptional learning and clustering
+
* conceptional learning and clustering
    * case-based reasoning and learning
+
* case-based reasoning and learning
    * similarity measures and learning of similarity
+
* similarity measures and learning of similarity
    * association rules
+
* association rules
    * visualization and data mining
+
* visualization and data mining
    * video mining
+
* video mining
    * mining structural representations such as log files, text documents and HTML documents
+
* mining structural representations such as log files, text documents and HTML documents
    * statistical learning and neural net based learning
+
* statistical learning and neural net based learning
    * classification and interpretation of images, text, video
+
* classification and interpretation of images, text, video
    * organisational learning and evolutional learning
+
* organisational learning and evolutional learning
    * probabilistic information retrieval
+
* probabilistic information retrieval
    * mining gene data bases and biological data bases
+
* mining gene data bases and biological data bases
    * mining images, temporal-spatial data, images from remote sensing
+
* mining images, temporal-spatial data, images from remote sensing
    * mining text documents
+
* mining text documents
    * knowledge extraction from text, video, signals and images
+
* knowledge extraction from text, video, signals and images
    * Classification and Model Estimation
+
* Classification and Model Estimation
    * Decision Trees
+
* Decision Trees
    * Case-Based Reasoning and Associative Memory
+
* Case-Based Reasoning and Associative Memory
    * Rule Induction and Grammars
+
* Rule Induction and Grammars
    * Neural Methods
+
* Neural Methods
    * Nonlinear Function Learning and Neural Net Based Learning
+
* Nonlinear Function Learning and Neural Net Based Learning
    * Support Vector Machines
+
* Support Vector Machines
    * Bayesian Models and Methods
+
* Bayesian Models and Methods
    * Subspace Methods
+
* Subspace Methods
    * Statistical and Conceptual Clustering Methods: Basics
+
* Statistical and Conceptual Clustering Methods: Basics
    * Applications of Clustering
+
* Applications of Clustering
    * Feature Grouping, Discretization, Selection and Transformation
+
* Feature Grouping, Discretization, Selection and Transformation
    * Feature Learning
+
* Feature Learning
    * Learning/adaption of recognition and perception
+
* Learning/adaption of recognition and perception
    * Learning of internal representations and models
+
* Learning of internal representations and models
    * Learning of appropriate behaviour
+
* Learning of appropriate behaviour
    * Learning of action patterns
+
* Learning of action patterns
    * Learning in Image Pre-Processing and Segmentation
+
* Learning in Image Pre-Processing and Segmentation
    * Statistical and Evolutionary Learning
+
* Statistical and Evolutionary Learning
    * Retrieval Methods
+
* Retrieval Methods
    * Content-Based Image Retrieval
+
* Content-Based Image Retrieval
    * Applications in Medicine
+
* Applications in Medicine
    * Time Series and Sequential Pattern Mining
+
* Time Series and Sequential Pattern Mining
    * Mining Financial or Stockmarket Data
+
* Mining Financial or Stockmarket Data
    * Frequent Pattern Mining
+
* Frequent Pattern Mining
    * Mining Images in Computer Vision
+
* Mining Images in Computer Vision
    * Mining Images and Texture
+
* Mining Images and Texture
    * Mining Motion from Sequence
+
* Mining Motion from Sequence
    * Real-Time Event Learning and Detection
+
* Real-Time Event Learning and Detection
    * Speech Analysis
+
* Speech Analysis
    * Aspects of Data Mining
+
* Aspects of Data Mining
    * Text Mining
+
* Text Mining
    * Symbolic Learning and Neural Networks in Document Processing
+
* Symbolic Learning and Neural Networks in Document Processing
    * Deviation and Novelty Detection
+
* Deviation and Novelty Detection
    * Learning and adaptive control
+
* Learning and adaptive control
    * Learning robots
+
* Learning robots
    * Learning in process automation
+
* Learning in process automation
    * Learning for Handwriting Recognition
+
* Learning for Handwriting Recognition
    * Network Analysis and Intrusion Detection
+
* Network Analysis and Intrusion Detection
    * Autoamtic Semantic Annotation of Media Content
+
* Autoamtic Semantic Annotation of Media Content
    * Learning of Semantic Inferencing Rules
+
* Learning of Semantic Inferencing Rules
    * Learning of Ontologies
+
* Learning of Ontologies
    * Learning of Visual Ontologies
+
* Learning of Visual Ontologies
    * High-Content Analysis of Microscopic Images in Medicine, Biotechnology and Chemistry
+
* High-Content Analysis of Microscopic Images in Medicine, Biotechnology and Chemistry
</pre>This CfP was obtained from [http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=3539&amp;copyownerid=1513 WikiCFP][[Category:Data mining]]
+
 
 +
 +
[[Category:Data mining]]

Latest revision as of 17:35, 1 March 2012

MLDM 2009
6th International Conference on Machine Learning and Data Mining
Dates Jul 23, 2009 (iCal) - Jul 25, 2009
Homepage: www.mldm.de
Location
Location: Leipzig, Germany
Loading map...

Important dates
Submissions: Jan 6, 2009
Notification: Mar 6, 2009
Camera ready due: Apr 27, 2009
Table of Contents


6th International Conference on Machine Learning and Data Mining

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
Facts about "MLDM 2009"
AcronymMLDM 2009 +
Camera ready dueApril 27, 2009 +
End dateJuly 25, 2009 +
Event typeConference +
Has coordinates51° 20' 26", 12° 22' 29"Latitude: 51.340633333333
Longitude: 12.374733333333
+
Has location cityLeipzig +
Has location countryCategory:Germany +
Homepagehttp://www.mldm.de +
IsAEvent +
NotificationMarch 6, 2009 +
Start dateJuly 23, 2009 +
Submission deadlineJanuary 6, 2009 +
Title6th International Conference on Machine Learning and Data Mining +