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

Cascade neural network for binary mapping.

G Martinelli1, F M Mascioli, G Bei

  • 1INFO-COM Dept., Roma Univ.

IEEE Transactions on Neural Networks
|January 1, 1993
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

Filgrastim XM02 (Tevagrastim®) after autologous stem cell transplantation compared to lenograstim: favourable cost-efficacy analysis.

Ecancermedicalscience·2013
Same author

Experimental demonstration of modulation instability in an optical fiber with a periodic dispersion landscape.

Optics letters·2012
Same author

Prediction of outcomes in patients with Ph+ chronic myeloid leukemia in chronic phase treated with nilotinib after imatinib resistance/intolerance.

Leukemia·2012
Same author

BCR-ABL-specific cytotoxic T cells in the bone marrow of patients with Ph(+) acute lymphoblastic leukemia during second-generation tyrosine-kinase inhibitor therapy.

Blood cancer journal·2012
Same author

Application of the whole-transcriptome shotgun sequencing approach to the study of Philadelphia-positive acute lymphoblastic leukemia.

Blood cancer journal·2012
Same author

Nilotinib in imatinib-resistant or imatinib-intolerant patients with chronic myeloid leukemia in chronic phase: 48-month follow-up results of a phase II study.

Leukemia·2012
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

A novel cascade architecture automatically determines the optimal number of neurons for neural networks performing binary mapping tasks. This approach, utilizing linear programming, also addresses network complexity and generalization.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Determining the optimal number of neurons in a neural network is a significant challenge for realizing specific binary mappings.
  • Existing methods often require extensive trial-and-error or complex heuristics.

Purpose of the Study:

  • To introduce a cascade architecture that automatically solves the problem of selecting the appropriate number of neurons for binary mapping neural networks.
  • To analyze the complexity and generalization capabilities of the proposed network architecture.

Main Methods:

  • A novel cascade architecture for neural networks is proposed.
  • The architecture employs an algorithm based on linear programming to determine network size.
  • The method facilitates automatic selection of neuron count for binary mapping.

Related Experiment Videos

Main Results:

  • The proposed cascade architecture effectively solves the problem of choosing the number of neurons for binary mapping.
  • The study discusses the complexity of the resulting neural network.
  • The generalization capability of the network is also analyzed.

Conclusions:

  • The cascade architecture offers an automated solution for designing neural networks for binary mappings.
  • The approach provides insights into network complexity and generalization performance.
  • This method simplifies neural network design for specific mapping tasks.