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

Comparison of two unsupervised algorithms

L Bobrowski, E R Caianiello

    Biological Cybernetics
    |January 1, 1980
    PubMed
    Summary
    This summary is machine-generated.

    This paper examines a model neuron using unsupervised learning. One of two self-learning algorithms, based on stochastic approximation, is proven to consistently converge to the correct decision rule in a given environment.

    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

    Phenotypic features of vascular calcification in chronic kidney disease.

    Journal of internal medicine·2019
    Same author

    Induction of similarity measures and medical diagnosis support rules through separable, linear data transformations.

    Methods of information in medicine·2006
    Same author

    Diagnosis supporting rules of the Hepar system.

    Studies in health technology and informatics·2001
    Same author

    Selection of medical tests based on linear nonseparability of data sets: a study on evaluation of liver diseases tests.

    Computers in biology and medicine·1984
    Same author

    A model for non-resolvable ambiguities.

    Biological cybernetics·1978
    Same author

    Learning processes in multilayer threshold nets.

    Biological cybernetics·1978
    Same journal

    Harmonic memory in phasor neural networks.

    Biological cybernetics·2026
    Same journal

    Correction: Decreased spinal inhibition leads to undiversified locomotor patterns.

    Biological cybernetics·2026
    Same journal

    Foundational issues of network models in biology.

    Biological cybernetics·2026
    Same journal

    Dynamical mechanisms for coordinating long-term working memory based on the precision of spike-timing in cortical neurons.

    Biological cybernetics·2026
    Same journal

    Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.

    Biological cybernetics·2026
    Same journal

    Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.

    Biological cybernetics·2026
    See all related articles

    Area of Science:

    • Computational Neuroscience
    • Machine Learning

    Background:

    • This study investigates a binary decision element, modeled as a neuron, operating within an unknown stationary environment.
    • The neuron receives independent input signals governed by a stationary distribution.

    Purpose of the Study:

    • To analyze unsupervised learning algorithms for adjusting a neuron's weight vector.
    • To compare two self-learning algorithms based on stochastic approximation.

    Main Methods:

    • The study employs two self-learning algorithms of the stochastic approximation type.
    • Both algorithms incorporate a rule for neglecting past experiences (weight decrease).
    • The algorithms differ in their weight increase rules.

    Main Results:

    Related Experiment Videos

    • It has been mathematically proven that only one of the two proposed algorithms consistently converges to a stable decision rule.
    • This convergence is demonstrated within a given stationary input distribution.

    Conclusions:

    • The findings highlight the importance of specific learning rules for achieving reliable convergence in neural models.
    • One self-learning algorithm demonstrates superior performance in establishing a consistent decision-making process.