Please use this identifier to cite or link to this item:
Title: Kernel machines and classifier ensemble learning for biomedical applications
Authors: Peng, Li
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics
Issue Date: 2006
Source: Peng, L. (2006). Kernel machines and classifier ensemble learning for biomedical applications. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: 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.
DOI: 10.32657/10356/3452
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
EEE-THESES_1312.pdf8.33 MBAdobe PDFThumbnail

Page view(s) 50

Updated on Apr 15, 2021

Download(s) 20

Updated on Apr 15, 2021

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.