Conference Information
MLDM 2020: International Conference on Machine Learning and Data Mining
Submission Date:
2020-02-15 Extended
Notification Date:
Conference Date:
New York City, New York, USA
QUALIS: b2   Viewed: 13079   Tracked: 22   Attend: 2

Conference Location
Call For Papers
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.

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
    Classification & Prediction
    Statistical Learning
    Association Rules
    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
    Graph Mining
    Agent Data Mining
    Applications in Software Testing
Last updated by Dou Sun in 2020-01-28
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