MLDM 2023

Call for Papers

Call for Paper MLDM 2023

Chair 

Petra Perner Institute of Computer Vision and Applied Computer Sciences IBaI, Germany

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

• 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 & 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 and Data Mining
• Deviation and Novelty Detection
• Feature Grouping, Discretization, Selection and Transformation
• Feature Learning
• Frequent Pattern Mining
• Learning and Adaptive Control
• Learning/Adaption of Recognition and Perception
• Learning for Handwriting Recognition
• Learning in Image Pre-Processing and Segmentation
• Mining Financial or Stockmarket Data
• Mining Motion from Sequence
• Subspace Methods
• Support Vector Machines
• Time Series and Sequential Pattern Mining
• Desirabilities
• Graph Mining
• Agent Data Mining
• Applications in Software Testing

Authors can submit their paper in long or short version.

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. Accepted long papers will be published by Springer Verlag in the LNAI Series in the book Advances in Data Mining, edited by Petra Perner.

Short papers are also welcome and can be used to describe work in progress or project ideas. They can have 5 to max. 15 pages, formatted in Springer LNCS format. Accepted short papers will be presented as poster in the poster session. They will be published in a special poster proceedings book.

Submission Deadline January 15th, 2023