MLDM 2023
18th International Conference on Machine Learning and Data Mining
July 15 – 19, 2023, New York, USA
Dear Authors and Participants,
Come and join us for the most exciting event in Machine Learning and Data Mining. We are looking forward to welcome you at our great event in New York.
Sincerely your,
Prof. Dr. Petra Perner
Chair
Petra Perner 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
Mark J. Embrechts Rensselaer Polytechnic Institute and CardioMag Imaging, Inc, USA
Robert Haralick City University of New York, USA
Adam Krzyzak Concordia University, Canada
Chengjun Liu New Jersey Institute of Technology, USA
Krzysztof Pancerz University Rzeszow, Poland
Dan Simovici University of Massachusetts Boston, USA
Agnieszka Wosiak Lodz University of Technology, Poland
more to be annouced…
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.
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Topics of the conference
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:
association rules
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.
Long Paper
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.
Short Paper
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.