MLDM 2021 : 17th International Conference on Machine Learning and Data Mining

Paper submissions should be related but not limited to any of the following topics:
• Applications in Medicine
• Applications of Clustering
• Aspects of Data Mining
• Association Rules
• Audio Mining
• Autoamtic Semantic Annotation of Media Content
• Bayesian Models and Methods
• Case-Based Reasoning and Associative Memory
• case-based reasoning and learning
• classification and interpretation of images, text, video
• Classification and Model Estimation
• Cognition and Computer Vision
• conceptional learning and clustering
• Content-Based Image Retrieval
• Decision Trees
• Deviation and Novelty Detection
• Feature Grouping, Discretization, Selection and Transformation
• Feature Learning
• Frequent Pattern Mining
• Goodness measures and evaluaion (e.g. false discovery rates)
• 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 for robots
• 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/adaption of recognition and perception
• 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 Social Media
• 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 bias
• Selection with small samples
• similarity
• similarity measures and learning
• Speech Analysis
• Statistical and Conceptual Clustering Methods: Basics
• Statistical and Evolutionary Learning
• statistical learning and neural net-based learning
• Subspace Methods
• Support Vector Machines
• Symbolic Learning and Neural Networks in Document Processing
• Time Series and Sequential Pattern Mining
• video mining
• visualization and data mining