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

New York, USA
Event Date: Jul 17, 2021 - Jul 22, 2021
Submission Deadline: Jan 15, 2021
Notification of Acceptance: Mar 18, 2021
Camera Ready Version Due : Apr 05, 2021

About

The MLDM´2021 conference is the fourteenth event in a series of Machine Learning and Data Mining meetings. The aim of MLDM is to bring together from all over the world researchers dealing with machine learning and data mining, in order to discuss the recent status of the research in the field and to direct its further developments.

Call For Paper

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

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