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

Circular backpropagation networks embed vector quantization.

S Ridella, S Rovetta, R Zunino

    IEEE Transactions on Neural Networks
    |February 7, 2008
    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

    Imaging artifacts in MRI simulators.

    The neuroradiology journal·2013
    Same author

    Study of bound water of poly-adenine using high frequency dielectric measurements.

    Biophysical journal·2009
    Same author

    Representation and generalization properties of class-entropy networks.

    IEEE transactions on neural networks·2008
    Same author

    Worst case analysis of weight inaccuracy effects in multilayer perceptrons.

    IEEE transactions on neural networks·2008
    Same author

    Empirical measure of multiclass generalization performance: the K-winner machine case.

    IEEE transactions on neural networks·2008
    Same author

    K-winner machines for pattern classification.

    IEEE transactions on neural networks·2008
    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 demonstrates the equivalence between vector quantization (VQ) classifiers and circular backpropagation (CBP) networks. VQ prototypes can initialize CBP networks, improving optimization for classification tasks like handwritten digit recognition.

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Pattern Recognition

    Background:

    • Vector Quantization (VQ) is a data compression technique.
    • Circular Backpropagation (CBP) networks are a type of neural network.
    • Both VQ and CBP are used in classification tasks.

    Discussion:

    • This letter establishes a formal equivalence between VQ classifiers and CBP networks.
    • It shows that VQ prototypes can be directly used in CBP feedforward structures.
    • This equivalence allows for effective initialization of CBP networks using VQ prototypes.

    Key Insights:

    • VQ classifiers and CBP networks are mathematically equivalent.
    • VQ prototypes provide a meaningful initialization strategy for CBP optimization.

    Related Experiment Videos

  • This approach was validated on the NIST handwritten digits dataset.

    • Outlook:

    • Potential for improved performance in classification tasks.
    • Further exploration of VQ-CBP network applications.
    • Development of new hybrid models combining VQ and CBP strengths.