A NEW HYBRID EXPANSION FUNCTION BASED MUTUAL INFORMATION FOR A MULTILAYER NEURAL NETWORKS OPTIMIZATION

Simple item page
dc.contributor.authorNcibi, Kais
dc.contributor.authorDjenina, Amor
dc.contributor.authorSadraoui, Tarek
dc.contributor.authorMILI, Faycel
dc.date.accessioned2023-10-02T11:17:07Z
dc.date.available2023-10-02T11:17:07Z
dc.date.issued2017
dc.description.abstractFunction expansion was used to expand initial features based on a non linear transformation. Many known expansion functions are found such the trigonometric, the polynomial, the Legendre polynomial, the power series, the exponential and the logarithmic transformation. This paper present a comparison between different expansion functions based on mutual information and different performance functions. We propose a new expansion process able to improve the correspondent mutual information and the final performance. The process was tested; using different benchmark databases, and shows his ability to improve results of classification problemsar
dc.identifier.issn2602-5655
dc.identifier.urihttp://hdl.handle.net/123456789/16828
dc.language.isoenar
dc.publisherOum-El-Bouaghi Universityar
dc.subjectFunction expansionar
dc.subjectMultilayer perceptronar
dc.subjectClassificationar
dc.subjectMutual informationar
dc.subjectFeatures selectionar
dc.titleA NEW HYBRID EXPANSION FUNCTION BASED MUTUAL INFORMATION FOR A MULTILAYER NEURAL NETWORKS OPTIMIZATIONar
dc.typeArticlear
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
A NEW HYBRID EXPANSION FUNCTION BASED MUTUAL.pdf
Size:
811.15 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
المقــــــــــــــــــالات