Enhanced kernel methods for pattern classification

A kernel method named least squares support vector machine is investigated in sub-topics of the pattern classification area.

Bibliographic Details
Main Author: Tang, Ke
Other Authors: Xin Yao
Format: Thesis
Published: 2008
Subjects:
Online Access:https://hdl.handle.net/10356/3346
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author Tang, Ke
author2 Xin Yao
author_facet Xin Yao
Tang, Ke
author_sort Tang, Ke
collection NTU
description A kernel method named least squares support vector machine is investigated in sub-topics of the pattern classification area.
first_indexed 2025-02-19T03:53:44Z
format Thesis
id ntu-10356/3346
institution Nanyang Technological University
last_indexed 2025-02-19T03:53:44Z
publishDate 2008
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spelling ntu-10356/33462023-07-04T17:27:49Z Enhanced kernel methods for pattern classification Tang, Ke Xin Yao Ponnuthurai N. Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition A kernel method named least squares support vector machine is investigated in sub-topics of the pattern classification area. DOCTOR OF PHILOSOPHY (EEE) 2008-09-17T09:27:57Z 2008-09-17T09:27:57Z 2007 2007 Thesis Tang, K. (2007). Enhanced kernel methods for pattern classification. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/3346 10.32657/10356/3346 Nanyang Technological University application/pdf
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Tang, Ke
Enhanced kernel methods for pattern classification
title Enhanced kernel methods for pattern classification
title_full Enhanced kernel methods for pattern classification
title_fullStr Enhanced kernel methods for pattern classification
title_full_unstemmed Enhanced kernel methods for pattern classification
title_short Enhanced kernel methods for pattern classification
title_sort enhanced kernel methods for pattern classification
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
url https://hdl.handle.net/10356/3346
work_keys_str_mv AT tangke enhancedkernelmethodsforpatternclassification