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

Generative and discriminative learning by CL-Net.

Yanmin Sun, Andrew K C Wong, Yang Wang

    IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
    |August 19, 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

    Benchmarking Interpretability in Healthcare Using Pattern Discovery and Disentanglement.

    Bioengineering (Basel, Switzerland)·2025
    Same author

    An Unsupervised Error Detection Methodology for Detecting Mislabels in Healthcare Analytics.

    Bioengineering (Basel, Switzerland)·2024
    Same author

    Theory and rationale of interpretable all-in-one pattern discovery and disentanglement system.

    NPJ digital medicine·2023
    Same author

    Pattern discovery and disentanglement on relational datasets.

    Scientific reports·2021
    Same author

    Explanation and prediction of clinical data with imbalanced class distribution based on pattern discovery and disentanglement.

    BMC medical informatics and decision making·2021
    Same author

    Discovery and disentanglement of aligned residue associations from aligned pattern clusters to reveal subgroup characteristics.

    BMC medical genomics·2018
    Same journal

    Strategic Ability Updating in Concurrent Games by Coalitional Commitment.

    IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2015
    Same journal

    Meta-Analysis of the First Facial Expression Recognition Challenge.

    IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
    Same journal

    Adjustable model-based fusion method for multispectral and panchromatic images.

    IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
    Same journal

    Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition.

    IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
    Same journal

    A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.

    IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
    Same journal

    Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.

    IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
    See all related articles

    This study introduces CL-Net, a novel two-stage classification algorithm. It combines generative and discriminative learning for improved classification accuracy in discrete spaces.

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Pattern Recognition

    Background:

    • Traditional classification methods often rely on purely generative or discriminative approaches.
    • Generative models capture data distribution, while discriminative models focus on decision boundaries.
    • Integrating both offers potential for enhanced performance but presents algorithmic challenges.

    Discussion:

    • The proposed CL-Net algorithm employs a two-stage approach for classification learning.
    • Stage one utilizes Chow-Liu (CL) method for approximating class-conditional distributions using a dependence tree.
    • Stage two employs a network to learn class posterior probabilities, focusing on discriminative aspects.

    Key Insights:

    • CL-Net effectively integrates generative and discriminative learning principles.

    Related Experiment Videos

  • The use of CL dependence-tree estimation in the first stage captures inherent class information.
  • Empirical results demonstrate significant performance improvements over existing generative or discriminative classifiers.

    • Outlook:

    • CL-Net offers a promising hybrid approach for complex classification tasks.
    • Further research could explore its application in various domains like image recognition and natural language processing.
    • Optimization of the network architecture in the second stage may yield further gains.