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MLDM 2023 : 19th International Conference on Machine Learning and Data Mining

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Link: http://www.mldm.de
 
When Jul 16, 2023 - Jul 21, 2023
Where New York, USA
Submission Deadline Feb 15, 2023
Notification Due Mar 18, 2023
Final Version Due Apr 5, 2023
Categories    machine learning   data mining   pattern recognition   classification
 

Call For Papers

19th International Conference on Machine Learning and Data Mining MLDM 2023
July 15 - 19, 2023, New York, USA
www.mldm.de

The submission deadline has been extended. The new Submission Deadline is February 15, 2023!


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.

Chair
Petra Perner Institute of Computer Vision and applied Computer Sciences IBaI, Germany

Program Committee
Piotr Artiemjew University of Warmia and Mazury in Olsztyn, Poland
Sung-Hyuk Cha Pace Universtity, USA
Ming-Ching Chang University of Albany, USA
Robert Haralick City University of New York, USA
Chih-Chung Hsu National Cheng Kung University, Taiwan
Adam Krzyzak Concordia University, Canada
Krzysztof Pancerz University Rzeszow, Poland
Dan Simovici University of Massachusetts Boston, USA
Tanveer Syeda-Mahmood IBM Almaden Research Center, USA
Yi Wei Samsung Research America Inc., USA
Agnieszka Wosiak Lodz University of Technology, Poland
more to be annouced...


Topics of the conference

Paper submissions should be related but not limited to any of the following topics:

Association Rules
Audio Mining
Autoamtic Semantic Annotation of Media Content
Bayesian Models and Methods
Capability Indices
Case-Based Reasoning and Associative Memory
case-based reasoning and learning
Classification & Prediction
classification and interpretation of images, text, video
Classification and Model Estimation
Clustering
Cognition and Computer Vision
Conceptional Learning
conceptional learning and clustering
Content-Based Image Retrieval
Control Charts
Decision Trees
Design of Experiment
Desirabilities
Deviation and Novelty Detection
Feature Grouping, Discretization, Selection and Transformation
Feature Learning
Frequent Pattern Mining
Goodness measures and evaluaion (e.g. false discovery rates)
Graph Mining
High-Content Analysis of Microscopic Images in Medicine, Biotechnology and Chemistry
Inductive Learning Including Decision Tree and Rule Induction Learning
knowledge extraction from text, video, signals and images
Learning and Adaptive Control
Learning for Handwriting Recognition
Learning in Image Pre-Processing and Segmentation
Learning in process automation
Learning of action patterns
Learning of appropriate behaviour
Learning of internal representations and models
Learning of Ontologies
Learning of Semantic Inferencing Rules
Learning of Visual Ontologies
Learning robots
Learning/Adaption of Recognition and Perception
Mining Financial or Stockmarket Data
Mining Gene Data Bases and Biological Data Bases
Mining Images and Texture
Mining Images in Computer Vision
Mining Images, Temporal-Spatial Data, Images from Remote Sensing
Mining Motion from Sequence
mining structural representations such as log files, text documents and HTML documents
mining text documents
Network Analysis and Intrusion Detection
Neural Methods
Nonlinear Function Learning and Neural Net Based Learning
Organisational Learning and Evolutional Learning
Probabilistic Information Retrieval
Real-Time Event Learning and Detection
Retrieval Methods
Rule Induction and Grammars
Sampling methods
Selection with small samples
Similarity Measures and Learning of Similarity
Speech Analysis
Statistical and Conceptual Clustering Methods
Statistical and Evolutionary Learning
Statistical Learning
Statistical Learning and Neural Net Based Learning
Strategy of Experimentation
Subspace Methods
Support Vector Machines
Symbolic Learning and Neural Networks in Document Processing
Telecommunication
Time Series and Sequential Pattern Mining
Video Mining
Visualization and Data Mining
Agent Data Mining
Applications in Medicine
Applications in Software Testing
Applications of Clustering
Aspects of Data Mining

Paper Submission
The paper must be formatted in the Springer LNCS format. They should have at most 15 pages. The papers will be reviewed by the program committee. The papers will be published in the conference proceedings.

Extended versions of the papers will appear in the Special Issue in the Intern. Journal Transaction on Machine Learning and Data Mining or in the Intern. Journal Transaction on
Case-Based Reasoning.

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