Deep web interfaces classification using QCGBP network

Author

Zhang, Liang ; Lu, Yuliang ; Liu, Jinhong

Author_Institution

Dept. of Network Eng., Electron. Eng. Inst., Hefei, China

fYear

2010

fDate

24-27 Aug. 2010

Firstpage

457

Lastpage

461

Abstract

This paper proposes a quantum neural network model using conjugate gradient back-propagation algorithm or QCGBP. The QCGBP model is constructed explicitly. Meanwhile, the learning rules for parameters of the network are also presented. According to the experiment result on TEL-8 datasets, QCGBP network presents better performance than conventional BP network in accelerating the convergence rate without decreasing the precise of classification.

Keywords

Internet; backpropagation; conjugate gradient methods; neural nets; pattern classification; quantum computing; QCGBP network; conjugate gradient back-propagation algorithm; convergence rate; deep Web interfaces classification; quantum neural network model; Artificial neural networks; Classification algorithms; Computational modeling; Convergence; Neurons; Quantum computing; Training; conjugate gradient back-propagation; deep Web; interface classification; quantum neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Education (ICCSE), 2010 5th International Conference on

Conference_Location

Hefei

Print_ISBN

978-1-4244-6002-1

Type

conf

DOI

10.1109/ICCSE.2010.5593580

Filename

5593580

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2451409