Extreme Learning Machine for Mammographic Risk Analysis

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

22 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

The assessment of mammographic risk analysis is an important issue in the medical field. Various approaches have been applied in order to achieve a higher accuracy in such analysis. In this paper, an approach known as Extreme Learning Machines (ELM), is employed to generate a single hidden layer neural network based classifier for estimating mammographic risk. ELM is able to avoid problems such as local minima, improper learning rate, and overfitting which iterative learning methods tend to suffer from. In addition the training phase of ELM is very fast. The performance of the ELM-trained neural network is compared with a number of state of the art classifiers. The results indicate that the use of ELM entails better classification accuracy for the problem of mammographic risk analysis.
Iaith wreiddiolSaesneg
TeitlProceedings of the 2010 UK Workshop on Computational Intelligence
CyhoeddwrIEEE Press
StatwsCyhoeddwyd - Medi 2010
DigwyddiadUKCI 2010: UK workship on Computational Intelligence - University of Essex, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Hyd: 08 Medi 201010 Medi 2010

Cynhadledd

CynhadleddUKCI 2010: UK workship on Computational Intelligence
Gwlad/TiriogaethTeyrnas Unedig Prydain Fawr a Gogledd Iwerddon
DinasUniversity of Essex
Cyfnod08 Medi 201010 Medi 2010

Ôl bys

Gweld gwybodaeth am bynciau ymchwil 'Extreme Learning Machine for Mammographic Risk Analysis'. Gyda’i gilydd, maen nhw’n ffurfio ôl bys unigryw.

Dyfynnu hyn