Systematic and effective supervised learning mechanism based on Jacobian rank deficiency

Guian Zhou, Jennie Si

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Most of neural network applications rely on the fundamental approximation property of feedforward networks. In a realistic problem setting, a mechanism is needed to devise this learning process based on available data, starting from choosing an appropriate set of parameters in order to avoid overfitting, to an efficient learning algorithm measured by computation and memory complexities, as well as the accuracy of the training procedure (measured by the training error), and not to forget testing and cross-validation for generalization. Many of these aspects have been addressed in literature, however individually or ineffectively. In the present paper we develop a comprehensive procedure to address the above issues in a systematic manner. This process is based on a common observation of Jacobian rank deficiency. A new numerical procedure for solving the nonlinear optimization problem in supervised learning is introduced which not only reduces the training time and overall complexity but also achieves good training accuracy and generalization.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
PublisherIEEE
Pages2399-2403
Number of pages5
Volume4
StatePublished - 1997
EventProceedings of the 1997 American Control Conference. Part 3 (of 6) - Albuquerque, NM, USA
Duration: Jun 4 1997Jun 6 1997

Other

OtherProceedings of the 1997 American Control Conference. Part 3 (of 6)
CityAlbuquerque, NM, USA
Period6/4/976/6/97

ASJC Scopus subject areas

  • Control and Systems Engineering

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