Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Density-induced support vector data description.

Kiyoung Lee, Dae-Won Kim, Kwang H Lee

    IEEE Transactions on Neural Networks
    |February 7, 2007
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    Lycopene pathway rewiring by combinatorial co-expression of constituent enzymes in a cell-free protein synthesis system.

    New biotechnology·2026
    Same author

    Vascular RhoJ Is an Effective and Selective Target for Tumor Angiogenesis and Vascular Disruption.

    Cancer cell·2026
    Same author

    Tunable Dynamics via Dual-Ion Modulation for Event-based Data Processing Using a Highly Uniform and Self-Rectifying Memristor Array.

    Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
    Same author

    Hierarchical ceria nanoarchitecture enabling accelerated lattice oxygen activation for efficient redox reactions.

    Nature communications·2026
    Same author

    Deep learning-based assessment of coronary artery disease using curved-multiplanar reconstruction: comprehensive evaluation of stenosis, calcification, and plaque.

    Frontiers in artificial intelligence·2026
    Same author

    De novo identification of potent ingredients for proteasome activation in MT101-5 using an AI-driven approach.

    Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2026
    Same journal

    Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

    IEEE transactions on neural networks·2013
    Same journal

    Guest editorial: special section on white box nonlinear prediction models.

    IEEE transactions on neural networks·2011
    Same journal

    Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

    IEEE transactions on neural networks·2011
    Same journal

    Guest editorial: special section on data-based control, modeling, and optimization.

    IEEE transactions on neural networks·2011
    Same journal

    Neural network-based multiple robot simultaneous localization and mapping.

    IEEE transactions on neural networks·2011
    Same journal

    Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

    IEEE transactions on neural networks·2011
    See all related articles

    This study introduces a novel Support Vector Data Description (SVDD) method using relative density to improve data description accuracy. The enhanced SVDD method shows promising results in protein localization prediction tasks.

    Area of Science:

    • Machine Learning
    • Data Mining
    • Bioinformatics

    Background:

    • Conventional Support Vector Data Description (SVDD) models hyperspherical data descriptions using support vectors.
    • SVDD may fail to capture optimal data characteristics when support vectors are not representative of the entire dataset.

    Discussion:

    • A new SVDD approach is proposed, incorporating relative density degree for distance measurements to better reflect data distribution.
    • This method enhances the accuracy of compact data descriptions, addressing limitations of traditional SVDD.

    Key Insights:

    • The proposed SVDD method improves data description by considering the relative density of data points.
    • This approach enhances the robustness of SVDD, particularly when dealing with non-representative support vectors.

    Related Experiment Videos

    Outlook:

    • The enhanced SVDD method is extended for multiclass, multilabel protein localization prediction.
    • Experimental results on real datasets demonstrate the effectiveness and potential of the proposed SVDD methodology.