Optimization Methods In Training Neural Networks

Terdapat beberapa teknik pengekstremuman bagi menyelesaikan masalah aIjabar linear dan tak linear. Kaedah Newton mempunyai sifat yang dipanggil penamatan kuadratik yang bermaksud ia meminimumkan suatu fungsi kuadratik dalam bilangan le1aran yang terhingga. Walaubagaimanapun, kaedah ini memerlukan...

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Main Author:	Sathasivam, Saratha
Format:	Thesis
Language:	English
Published:	2003
Subjects:	QA1 Mathematics (General)
Online Access:	http://eprints.usm.my/31158/1/SARATHA_SATHASIVAM.pdf
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id	my-usm-ep.31158
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spelling	my-usm-ep.311582017-07-27T04:46:59Z Optimization Methods In Training Neural Networks 2003-07 Sathasivam, Saratha QA1 Mathematics (General) Terdapat beberapa teknik pengekstremuman bagi menyelesaikan masalah aIjabar linear dan tak linear. Kaedah Newton mempunyai sifat yang dipanggil penamatan kuadratik yang bermaksud ia meminimumkan suatu fungsi kuadratik dalam bilangan le1aran yang terhingga. Walaubagaimanapun, kaedah ini memerlukan pengiraan dan pengstoran terbitan kedua bagi fungsi kuadratik yang terlibat. Apabila bilangan parameter n adalah besar, ianya mungkin tidak praktikat· untuk mengira semua terbitap kedua. Hal ini adalah benar bagi rangkaian neural di mana kebanyakan aplikasi praktikal memerlukan beberapa ratus atau ribu pemberat. Bagi masalah-masalah sedemikian, kaedah pengoptimuman yang hanya memerlukan terbitan pertama tetapi masih mempunyai sifat penamatan kuadratik lebih diutamakan. There are a number of extremizing techniques to solve linear and nonlinear algebraic • problems. Newton's method has a property called quadratic termination~ which means that it minimizes a quadratic function exactly in a finite number of iterations. Unfortunately, it requires calculation and storage of the second derivatives of the quadratic function involved. When the number of parameters, n, is large, it may be impractical to compute all the second derivatives. This is especially true for neural networks, where practical applications can require several hundred to many thousands weights. Eor these particular cases, methods that require ,only first derivatives bMt still have quadratic termination are preferred. 2003-07 Thesis http://eprints.usm.my/31158/ http://eprints.usm.my/31158/1/SARATHA_SATHASIVAM.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Sains Matematik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA1 Mathematics (General)
spellingShingle	QA1 Mathematics (General) Sathasivam, Saratha Optimization Methods In Training Neural Networks
description	Terdapat beberapa teknik pengekstremuman bagi menyelesaikan masalah aIjabar linear dan tak linear. Kaedah Newton mempunyai sifat yang dipanggil penamatan kuadratik yang bermaksud ia meminimumkan suatu fungsi kuadratik dalam bilangan le1aran yang terhingga. Walaubagaimanapun, kaedah ini memerlukan pengiraan dan pengstoran terbitan kedua bagi fungsi kuadratik yang terlibat. Apabila bilangan parameter n adalah besar, ianya mungkin tidak praktikat· untuk mengira semua terbitap kedua. Hal ini adalah benar bagi rangkaian neural di mana kebanyakan aplikasi praktikal memerlukan beberapa ratus atau ribu pemberat. Bagi masalah-masalah sedemikian, kaedah pengoptimuman yang hanya memerlukan terbitan pertama tetapi masih mempunyai sifat penamatan kuadratik lebih diutamakan. There are a number of extremizing techniques to solve linear and nonlinear algebraic • problems. Newton's method has a property called quadratic termination~ which means that it minimizes a quadratic function exactly in a finite number of iterations. Unfortunately, it requires calculation and storage of the second derivatives of the quadratic function involved. When the number of parameters, n, is large, it may be impractical to compute all the second derivatives. This is especially true for neural networks, where practical applications can require several hundred to many thousands weights. Eor these particular cases, methods that require ,only first derivatives bMt still have quadratic termination are preferred.
format	Thesis
qualification_level	Master's degree
author	Sathasivam, Saratha
author_facet	Sathasivam, Saratha
author_sort	Sathasivam, Saratha
title	Optimization Methods In Training Neural Networks
title_short	Optimization Methods In Training Neural Networks
title_full	Optimization Methods In Training Neural Networks
title_fullStr	Optimization Methods In Training Neural Networks
title_full_unstemmed	Optimization Methods In Training Neural Networks
title_sort	optimization methods in training neural networks
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Matematik
publishDate	2003
url	http://eprints.usm.my/31158/1/SARATHA_SATHASIVAM.pdf
_version_	1747820400697409536

Optimization Methods In Training Neural Networks

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