Pattern discrimination method with a boosting approach using hierarchical neural trees-Reference-Cited by-同舟云学术

Pattern discrimination method with a boosting approach using hierarchical neural trees

Published:2008-10-08 Issue:7 Volume:222 Page:701-710
ISSN:0959-6518
Container-title:Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
language:en
Short-container-title:Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering

Author:

Okamoto M¹,Shima K²,Matsubara Y¹,Tsuji T²

Affiliation:

1. Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan

2. Graduate School of Engineering, Hiroshima University, Hiroshima, Japan

Abstract

This paper proposes a new pattern classification method using probabilistic neural networks based on a boosting approach. In this method, a log-linearized Gaussian mixture network is used as a weak classifier. The method proposed automatically constructs a suitable classification network from given data. Validity of the proposed method is shown by discrimination results of artificial data and hand shape. The application is confirmed of the proposed method to human interface controlling of home electric appliances using hand shapes.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Control and Systems Engineering

Link

http://journals.sagepub.com/doi/pdf/10.1243/09596518JSCE563

Reference13 articles.

1. A log-linearized Gaussian mixture network and its application to EEG pattern classification

2. Bio-mimetic Control of An Externally Powered Prosthetic Forearm Based on EMG Signals.

3. An EMG controlled pointing device using a neural network

4. A human-assisting manipulator teleoperated by EMG signals and arm motions

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