Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Efficient block training of multilayer perceptrons.

A Navia-Vázquez¹, A R Figueiras-Vidal

¹DTC, Universidad Carlos III de Madrid, Spain.

Neural Computation

|August 10, 2000

Summary

This study refines layerwise block training for multilayer perceptrons (MLP) by adding a sensitivity correction factor, significantly improving performance. The enhanced method shows advantages over existing techniques in various applications.

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

Generalizing CMAC architecture and training.

IEEE transactions on neural networks·2008

Same author

Cost functions to estimate a posteriori probabilities in multiclass problems.

IEEE transactions on neural networks·2008

Same author

Sample selection via clustering to construct support vector-like classifiers.

IEEE transactions on neural networks·2008

Same author

On the structure of strict sense Bayesian cost functions and its applications.

IEEE transactions on neural networks·2008

Same author

Weighted least squares training of support vector classifiers leading to compact and adaptive schemes.

IEEE transactions on neural networks·2008

Same author

Empirical risk minimization for support vector classifiers.

IEEE transactions on neural networks·2008

Area of Science:

Machine Learning
Artificial Intelligence
Neural Networks

Background:

Layerwise block training offers computational advantages for multilayer perceptrons (MLP).
Existing methods may lack optimal performance due to formulation limitations.

Purpose of the Study:

To enhance layerwise block training algorithms for MLPs.
To introduce a sensitivity correction factor for improved performance.
To analyze the theoretical underpinnings of the performance gains.

Main Methods:

Modification of layerwise block training algorithms.
Introduction of a sensitivity correction factor.
Empirical verification across several applications.
Theoretical analysis relating to second-order methods and Fisher's information matrix.

Main Results:

The refined algorithm demonstrates a clear performance advantage.
The sensitivity correction factor is shown to be crucial for the performance gains.
The method's effectiveness is validated through practical applications.

Conclusions:

The proposed sensitivity correction factor significantly enhances MLP training.
The approach offers a promising direction for improving neural network training efficiency and effectiveness.
Potential extensions to recurrent networks and other research areas are identified.

Related Experiment Videos