Probabilistic DBNN with applications to sensor fusion and object recognition

Shang Hung Lin; S. Y. Kung; Long Ji Lin

Probabilistic DBNN with applications to sensor fusion and object recognition

Shang Hung Lin
, S. Y. Kung
, Long Ji Lin

Research output: Contribution to conference › Paper › peer-review

Abstract

A probabilistic variant of the decision-based neural network (DBNN) that is meant to better estimate probability density functions corresponding to different pattern classes is developed. New learning rules for the probabilistic DBNN are derived. Experimental results demonstrate noticeable improvement in various performance measures such as recognition accuracies and, in particular, false acceptance/rejection rates. A multiple sensor fusion system for object classification is developed using probabilistic output values of the DBNN.

Original language	English (US)
Pages	333-342
Number of pages	10
State	Published - 1995
Event	Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95) - Cambridge, MA, USA Duration: Aug 31 1995 → Sep 2 1995

Other

Other	Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95)
City	Cambridge, MA, USA
Period	8/31/95 → 9/2/95

All Science Journal Classification (ASJC) codes

Signal Processing
Software
Electrical and Electronic Engineering

Cite this

@conference{eba09ee4c297401b9678382704d3edde,

title = "Probabilistic DBNN with applications to sensor fusion and object recognition",

abstract = "A probabilistic variant of the decision-based neural network (DBNN) that is meant to better estimate probability density functions corresponding to different pattern classes is developed. New learning rules for the probabilistic DBNN are derived. Experimental results demonstrate noticeable improvement in various performance measures such as recognition accuracies and, in particular, false acceptance/rejection rates. A multiple sensor fusion system for object classification is developed using probabilistic output values of the DBNN.",

author = "Lin, \{Shang Hung\} and Kung, \{S. Y.\} and Lin, \{Long Ji\}",

year = "1995",

language = "English (US)",

pages = "333--342",

note = "Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95) ; Conference date: 31-08-1995 Through 02-09-1995",

}

TY - CONF

T1 - Probabilistic DBNN with applications to sensor fusion and object recognition

AU - Lin, Shang Hung

AU - Kung, S. Y.

AU - Lin, Long Ji

PY - 1995

Y1 - 1995

N2 - A probabilistic variant of the decision-based neural network (DBNN) that is meant to better estimate probability density functions corresponding to different pattern classes is developed. New learning rules for the probabilistic DBNN are derived. Experimental results demonstrate noticeable improvement in various performance measures such as recognition accuracies and, in particular, false acceptance/rejection rates. A multiple sensor fusion system for object classification is developed using probabilistic output values of the DBNN.

AB - A probabilistic variant of the decision-based neural network (DBNN) that is meant to better estimate probability density functions corresponding to different pattern classes is developed. New learning rules for the probabilistic DBNN are derived. Experimental results demonstrate noticeable improvement in various performance measures such as recognition accuracies and, in particular, false acceptance/rejection rates. A multiple sensor fusion system for object classification is developed using probabilistic output values of the DBNN.

UR - https://www.scopus.com/pages/publications/0029217731

UR - https://www.scopus.com/pages/publications/0029217731#tab=citedBy

M3 - Paper

AN - SCOPUS:0029217731

SP - 333

EP - 342

T2 - Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95)

Y2 - 31 August 1995 through 2 September 1995

ER -

Probabilistic DBNN with applications to sensor fusion and object recognition

Abstract

Other

All Science Journal Classification (ASJC) codes

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