Choosing feature sets for training and testing self-organising maps: A case study

K Ahmad; BL Vrusias; A Ledford

doi:10.1007/s005210170018

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Choosing feature sets for training and testing self-organising maps: A case study

Journal article

Peer reviewed

Choosing feature sets for training and testing self-organising maps: A case study

K Ahmad, BL Vrusias and A Ledford

NEURAL COMPUTING & APPLICATIONS, Vol.10(1), pp.56-66

01/01/2001

DOI: https://doi.org/10.1007/s005210170018

Abstract

Science & Technology

Technology

Computer Science

Artificial Intelligence

Computer Science

COMPUTER SCIENCE

ARTIFICIAL INTELLIGENCE

automatic classification

Kohonen map

linear discriminant rule

SOFM

text classification

training NN

weirdness coefficient

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Title: Choosing feature sets for training and testing self-organising maps: A case study
Creators: K Ahmad
BL Vrusias
A Ledford
Publication Details: NEURAL COMPUTING & APPLICATIONS, Vol.10(1), pp.56-66
Publisher: SPRINGER-VERLAG
Date published: 01/01/2001
Date submitted: 17/05/2017
Identifiers: 99513750602346
Academic Unit: University of Surrey
Language: English
Resource Type: Journal article

Choosing feature sets for training and testing self-organising maps: A case study

Abstract

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