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

A study on SMO-type decomposition methods for support vector machines.

Pai-Hsuen Chen, Rong-En Fan, Chih-Jen Lin

    IEEE Transactions on Neural Networks
    |July 22, 2006
    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

    Rewiring the luteal microenvironment: hemodynamic and molecular insights into eCG-supported CL development in indigenous White Lamphun cattle.

    Reproduction & fertility·2025
    Same author

    An Overview of Advancements in Proteomic Approaches to Enhance Livestock Production and Aquaculture.

    Animals : an open access journal from MDPI·2025
    Same author

    Effect of eCG on Terminal Follicular Growth and Corpus Luteum Development and Blood Perfusion in Estrous-Synchronized White Lamphun Cattle.

    Animals : an open access journal from MDPI·2025
    Same author

    Extracellular vesicles secreted by cumulus cells contain microRNAs that are potential regulatory factors of mouse oocyte developmental competence.

    Molecular human reproduction·2024
    Same author

    One-Class SVM Probabilistic Outputs.

    IEEE transactions on neural networks and learning systems·2024
    Same author

    Recent advances in the understanding of tubal ectopic pregnancy.

    Faculty reviews·2023
    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 presents a flexible approach to sequential minimal optimization (SMO) for training support vector machines (SVMs). It offers a general convergence proof and demonstrates linear convergence for SVM training.

    Area of Science:

    • Machine Learning
    • Computational Science

    Background:

    • Decomposition methods are key for training support vector machines (SVMs).
    • Existing methods often rely on specific working set selection rules.
    • A need exists for more general and flexible decomposition approaches.

    Purpose of the Study:

    • To analyze sequential minimal optimization (SMO) type decomposition methods.
    • To investigate SVM training with a generalized two-element working set selection.
    • To provide theoretical insights into SMO convergence and techniques.

    Main Methods:

    • Analysis of SMO algorithms with a flexible two-element working set selection.
    • Development of an asymptotic convergence proof for SMO methods.
    • Explanation of shrinking and caching techniques in SVM training.

    Related Experiment Videos

    Main Results:

    • A simple asymptotic convergence proof for the generalized SMO method.
    • A comprehensive explanation of shrinking and caching strategies.
    • Demonstration of linear convergence for the analyzed decomposition methods.
    • Discussion of extensions to support vector machine variants.

    Conclusions:

    • The generalized SMO approach offers a flexible framework for SVM training.
    • The study provides theoretical guarantees for convergence and efficiency.
    • The findings contribute to a deeper understanding of decomposition methods for SVMs.