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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Bibliographic Details
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
Description
Summary: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.

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