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MLDM 2024 : 20th International Conference on Machine Learning and Data MiningConference Series : Machine Learning and Data Mining in Pattern Recognition | |||||||||||||||
Link: http://www.mldm.de | |||||||||||||||
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Call For Papers | |||||||||||||||
20th International Conference on Machine Learning and Data Mining MLDM 2024
July 13 - 18, 2024, Dresden, Germany www.mldm.de The submission deadline is Janurary 15, 2024! 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 Robert Haralick City University of New York, USA Chih-Chung Hsu National Cheng Kung University, Taiwan Adam Krzyzak Concordia University, Canada Krzysztof Pancerz University Rzeszow, Poland Dan Simovici University of Massachusetts Boston, USA Tanveer Syeda-Mahmood IBM Almaden Research Center, USA Yi Wei Samsung Research America Inc., 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 Goodness measures and evaluaion (e.g. false discovery rates) Graph Mining 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 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 robots Learning/Adaption of Recognition and Perception Mining Financial or Stockmarket Data 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 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 with small samples Similarity Measures and Learning of Similarity Speech Analysis Statistical and Conceptual Clustering Methods Statistical and Evolutionary Learning Statistical Learning Statistical Learning and Neural Net Based Learning Strategy of Experimentation Subspace Methods Support Vector Machines Symbolic Learning and Neural Networks in Document Processing Telecommunication Time Series and Sequential Pattern Mining Video Mining Visualization and Data Mining Agent Data Mining Applications in Medicine Applications in Software Testing Applications of Clustering Aspects of Data Mining Paper Submission 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. The papers will be published in the conference proceedings. https://easychair.org/conferences/?conf=mldm2024 Extended versions of the papers will appear in the Special Issue in the Intern. Journal Transaction on Machine Learning and Data Mining or in the Intern. Journal Transaction on Case-Based Reasoning. |
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