MLDM 2023 : 18th International Conference on Machine Learning and Data Mining
http://www.mldm.de
 
When    Jul 16, 2023 - Jul 21, 2023
Where    New York, USA
Submission Deadline    Jan 15, 2023
Notification Due    Mar 18, 2023
Final Version Due    Apr 5, 2023
Categories:    machine learning   data mining   pattern recognition   classification
 
Call For Papers
MLDM 2023
18th International Conference on Machine Learning and Data Mining
July 15 - 19, 2023, New York, USA

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
Mark J. Embrechts Rensselaer Polytechnic Institute and CardioMag Imaging, Inc, USA
Robert Haralick City University of New York, USA
Adam Krzyzak Concordia University, Canada
Chengjun Liu New Jersey Institute of Technology, USA
Krzysztof Pancerz University Rzeszow, Poland
Dan Simovici University of Massachusetts Boston, 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