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

Generalization and selection of examples in feedforward neural networks.

L Franco1, S A Cannas

  • 1Facultad de Matemática, Astronomía y Física, Universidad Nacional de Córdoba, Ciudad Universitaria, Argentina.

Neural Computation
|October 14, 2000
PubMed
Summary

Selecting specific training examples enhances boolean neural network learning. A new criterion improves generalization by reducing the number of examples needed, outperforming random sampling.

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

Evidence for the Collective Nature of Radial Flow in Pb+Pb Collisions with the ATLAS Detector.

Physical review letters·2026
Same author

Evidence for the Dimuon Decay of the Higgs Boson in pp Collisions with the ATLAS Detector.

Physical review letters·2025
Same author

Evidence for Longitudinally Polarized W Bosons in the Electroweak Production of Same-Sign W Boson Pairs in Association with Two Jets in pp Collisions at sqrt[s]=13  TeV with the ATLAS Detector.

Physical review letters·2025
Same author

Observation of tt[over ¯] Production in Pb+Pb Collisions at sqrt[s_{NN}]=5.02  TeV with the ATLAS Detector.

Physical review letters·2025
Same author

Search for Dark Matter Produced in Association with a Dark Higgs Boson in the bb[over ¯] Final State Using pp Collisions at sqrt[s]=13  TeV with the ATLAS Detector.

Physical review letters·2025
Same author

Search for Magnetic Monopole Pair Production in Ultraperipheral Pb+Pb Collisions at sqrt[s_{NN}]=5.36  TeV with the ATLAS Detector at the LHC.

Physical review letters·2025

Area of Science:

  • Machine Learning
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Boolean neural networks are fundamental models for studying learning.
  • The choice of training data significantly impacts learning efficiency and generalization.
  • Understanding generalization error is crucial for effective model training.

Purpose of the Study:

  • To investigate the effect of example selection on boolean neural network learning.
  • To analyze the relationship between function complexity, network architecture, and generalization.
  • To develop an architecture-independent criterion for optimal training set selection.

Main Methods:

  • Analytical calculation of the minimum number of examples for zero generalization error.
  • Numerical simulations to validate the proposed selection criterion.

Related Experiment Videos

  • Comparative analysis against random sampling and full dataset usage.

    • Main Results:

    • A general, architecture-independent criterion for training example selection was proposed.
    • The proposed criterion improved generalization capacity compared to random sampling across various scenarios.
    • For the parity problem, specific architectures require the full dataset for global learning, a limitation addressable by tree-structured networks.

    Conclusions:

    • Strategic example selection is key to enhancing generalization in boolean neural networks.
    • The developed criterion offers a method to optimize training data, reducing computational load.
    • Network architecture plays a critical role in learning complex functions, with specific structures like tree networks offering advantages.