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ICMLDM 2013 : International Conference on Machine Learning and Data Mining

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Link: https://www.waset.org/conferences/2013/amsterdam/icmldm/
 
When May 15, 2013 - May 16, 2013
Where Amsterdam, The Netherlands
Submission Deadline Dec 31, 2012
Notification Due Jan 31, 2013
Final Version Due Feb 28, 2013
Categories    data mining   machine learning   computer science   artificial intelligence
 

Call For Papers

The XXXIV. International Conference on Machine Learning and Data Mining is the premier forum for the presentation of new advances and research results in the fields of Machine Learning and Data Mining. The conference will bring together leading academic scientists, researchers and scholars in the domain of interest from around the world. Topics of interest for submission include, but are not limited to:

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

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