Kernel machines and classifier ensemble learning for biomedical applications

Kernel machines and classifier ensemble learning for biomedical applications

This thesis addressed a type of imbalanced data problem encountered in many biomedical applications where one category of data is compactly clustered and the other category of data is scattered in the input space. A new Hybrid Kernel Machine Ensemble (HKME) is proposed to address this problem by int...

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Main Author: Peng, Li
Other Authors: Shankar M. Krishnan
Format: Theses and Dissertations
Published: 2008
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics
Online Access:https://hdl.handle.net/10356/3452
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Institution: Nanyang Technological University
id sg-ntu-dr.10356-3452
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spelling sg-ntu-dr.10356-34522023-07-04T16:55:56Z Kernel machines and classifier ensemble learning for biomedical applications Peng, Li Shankar M. Krishnan Chan, Kap Luk School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics This thesis addressed a type of imbalanced data problem encountered in many biomedical applications where one category of data is compactly clustered and the other category of data is scattered in the input space. A new Hybrid Kernel Machine Ensemble (HKME) is proposed to address this problem by integrating a two-class discriminative Support Vector Machine (SVM) and a one-class recognition-based SVM. DOCTOR OF PHILOSOPHY (EEE) 2008-09-17T09:30:23Z 2008-09-17T09:30:23Z 2006 2006 Thesis Peng, L. (2006). Kernel machines and classifier ensemble learning for biomedical applications. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/3452 10.32657/10356/3452 Nanyang Technological University application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
topic DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics
Peng, Li
Kernel machines and classifier ensemble learning for biomedical applications
description This thesis addressed a type of imbalanced data problem encountered in many biomedical applications where one category of data is compactly clustered and the other category of data is scattered in the input space. A new Hybrid Kernel Machine Ensemble (HKME) is proposed to address this problem by integrating a two-class discriminative Support Vector Machine (SVM) and a one-class recognition-based SVM.
author2 Shankar M. Krishnan
author_facet Shankar M. Krishnan
Peng, Li
format Theses and Dissertations
author Peng, Li
author_sort Peng, Li
title Kernel machines and classifier ensemble learning for biomedical applications
title_short Kernel machines and classifier ensemble learning for biomedical applications
title_full Kernel machines and classifier ensemble learning for biomedical applications
title_fullStr Kernel machines and classifier ensemble learning for biomedical applications
title_full_unstemmed Kernel machines and classifier ensemble learning for biomedical applications
title_sort kernel machines and classifier ensemble learning for biomedical applications
publishDate 2008
url https://hdl.handle.net/10356/3452
_version_ 1772825385520594944

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