20th International Conference on Machine Learning and Data Mining (MLDM 2025)

Topics

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

Who should Attend

Attendees involved or interested in machine learning and data mining.