Seeking signals in data bases using neural networks

Author

Stebbins, George

Author_Institution

Adv. Policy Inst., California Univ., Los Angeles, CA, USA

Volume

1

fYear

1999

fDate

1999

Firstpage

167

Abstract

This paper presents a strategy to allow searching a database using information that is not originally in the database. A backpropagation neural network is proposed that learns correlations between data items in the database and generates predictions. This network technique is applied to the problem of predicting housing code violations, given census data, and other problem property data. A problem is discovered in determining the categorization of the data, and a possible solution is proposed

Keywords

backpropagation; correlation methods; data mining; neural nets; public administration; signal processing; backpropagation neural network; census data; damaged data; data categorization; data item correlation learning; data mining; database searching; housing code violations; knowledge discovery; missing data; predictions; property data; signal seeking; Artificial neural networks; Backpropagation; Cities and towns; Convergence; Databases; Information retrieval; Inspection; Intelligent networks; Neural networks; Self organizing feature maps;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Its Applications, 1999. ISSPA '99. Proceedings of the Fifth International Symposium on

Conference_Location

Brisbane, Qld.

Print_ISBN

1-86435-451-8

Type

conf

DOI

10.1109/ISSPA.1999.818139

Filename

818139