Building an accretive authentication system using a RBF network

Qiuming Zhu, Luzheng Liu

Research output: Contribution to conferencePaper

Abstract

A computerized authentication system should be able to admit new authentic entries continuously while maintain the existing entry records and an uninterrupted system operation. In this paper, we describe a competitive RBF neural network that is able to incrementally construct itself in response to the pattern samples presented to the system. The neural network is thus a suitable choice for authentication system applications. The accretion property of the neural network is made possible by allowing each pattern class (an authentic entry) being modeled in multiple hyper-ellipsoidal distributions, and mapping these distributions to multiple RBF neural units.

Original languageEnglish (US)
Pages2876-2881
Number of pages6
StatePublished - Dec 1 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period7/10/997/16/99

Fingerprint

Radial basis function networks
Authentication
Neural networks

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Zhu, Q., & Liu, L. (1999). Building an accretive authentication system using a RBF network. 2876-2881. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .

Building an accretive authentication system using a RBF network. / Zhu, Qiuming; Liu, Luzheng.

1999. 2876-2881 Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .

Research output: Contribution to conferencePaper

Zhu, Q & Liu, L 1999, 'Building an accretive authentication system using a RBF network' Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, 7/10/99 - 7/16/99, pp. 2876-2881.
Zhu Q, Liu L. Building an accretive authentication system using a RBF network. 1999. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .
Zhu, Qiuming ; Liu, Luzheng. / Building an accretive authentication system using a RBF network. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .6 p.
@conference{5f9d38f76a9e44b5b0b3824b3fc5f139,
title = "Building an accretive authentication system using a RBF network",
abstract = "A computerized authentication system should be able to admit new authentic entries continuously while maintain the existing entry records and an uninterrupted system operation. In this paper, we describe a competitive RBF neural network that is able to incrementally construct itself in response to the pattern samples presented to the system. The neural network is thus a suitable choice for authentication system applications. The accretion property of the neural network is made possible by allowing each pattern class (an authentic entry) being modeled in multiple hyper-ellipsoidal distributions, and mapping these distributions to multiple RBF neural units.",
author = "Qiuming Zhu and Luzheng Liu",
year = "1999",
month = "12",
day = "1",
language = "English (US)",
pages = "2876--2881",
note = "International Joint Conference on Neural Networks (IJCNN'99) ; Conference date: 10-07-1999 Through 16-07-1999",

}

TY - CONF

T1 - Building an accretive authentication system using a RBF network

AU - Zhu, Qiuming

AU - Liu, Luzheng

PY - 1999/12/1

Y1 - 1999/12/1

N2 - A computerized authentication system should be able to admit new authentic entries continuously while maintain the existing entry records and an uninterrupted system operation. In this paper, we describe a competitive RBF neural network that is able to incrementally construct itself in response to the pattern samples presented to the system. The neural network is thus a suitable choice for authentication system applications. The accretion property of the neural network is made possible by allowing each pattern class (an authentic entry) being modeled in multiple hyper-ellipsoidal distributions, and mapping these distributions to multiple RBF neural units.

AB - A computerized authentication system should be able to admit new authentic entries continuously while maintain the existing entry records and an uninterrupted system operation. In this paper, we describe a competitive RBF neural network that is able to incrementally construct itself in response to the pattern samples presented to the system. The neural network is thus a suitable choice for authentication system applications. The accretion property of the neural network is made possible by allowing each pattern class (an authentic entry) being modeled in multiple hyper-ellipsoidal distributions, and mapping these distributions to multiple RBF neural units.

UR - http://www.scopus.com/inward/record.url?scp=0033307281&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033307281&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0033307281

SP - 2876

EP - 2881

ER -