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    MLDM' 2018 - 14th International Conference on Machine Learning and Data Mining MLDM'2018

    View: 1907

    Website http://www.mldm.de/index.php | Want to Edit it Edit Freely

    Category MLDM' 2018

    Deadline: February 15, 2018 | Date: July 14, 2018-July 19, 2018

    Venue/Country: New York, U.S.A

    Updated: 2017-10-11 21:08:35 (GMT+9)

    Call For Papers - CFP

    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.

    All kinds of applications are welcome but special preference will be given to multimedia related applications, biomedical applications, and webmining. Paper submissions should be related but not limited to any of the following topics:

    association rules

    Audio Mining

    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

    Selection bias

    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 Financial or Stockmarket Data

    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: Basics

    Statistical and Evolutionary Learning

    Subspace Methods

    Support Vector Machines

    Symbolic Learning and Neural Networks in Document Processing

    Text Mining

    Time Series and Sequential Pattern Mining

    Mining Social Media


    Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
    Disclaimer: ourGlocal is an open academical resource system, which anyone can edit or update. Usually, journal information updated by us, journal managers or others. So the information is old or wrong now. Specially, impact factor is changing every year. Even it was correct when updated, it may have been changed now. So please go to Thomson Reuters to confirm latest value about Journal impact factor.