20th International Conference on Machine Learning and Data Mining

Event Dates

Jul 13, 2024 - Jul 18, 2024

Location

Dresden, Germany

Submission Deadline

Jan 15, 2024

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.