One-Class Support Vector Machine for Functional Data Novelty Detection

2012 ◽  
Author(s):  
Ma Yao ◽  
Huangang Wang
Keyword(s):  
Support Vector Machine ◽  
Functional Data ◽  
Novelty Detection ◽  
Support Vector

RANKING-BASED KERNELS IN APPLIED BIOMEDICAL DIAGNOSTICS USING A SUPPORT VECTOR MACHINE

2011 ◽  
Vol 21 (06) ◽  
pp. 459-473 ◽  
Author(s):  
VILEN JUMUTC ◽  
PAWEL ZAYAKIN ◽  
ARKADY BORISOV
Keyword(s):  
Support Vector Machine ◽  
Novelty Detection ◽  
Clinical Diagnostics ◽  
High Dimensional ◽  
Support Vector ◽  
Biomedical Data ◽  
Classification Rate ◽  
Generalization Bounds ◽  
Theoretical Generalization ◽  
Biomedical Diagnostics

This paper presents some essential findings and results on using ranking-based kernels for the analysis and utilization of high dimensional and noisy biomedical data in applied clinical diagnostics. We claim that presented kernels combined with a state-of-the-art classification technique — a Support Vector Machine (SVM) — could significantly improve the classification rate and predictive power of the wrapper method, e.g. SVM. Moreover, the advantage of such kernels could be potentially exploited for other kernel methods and essential computer-aided tasks such as novelty detection and clustering. Our experimental results and theoretical generalization bounds imply that ranking-based kernels outperform other traditionally employed SVM kernels on high dimensional biomedical and microarray data.

scholarly journals Support vector machine for functional data classification

2006 ◽  
Vol 69 (7-9) ◽  
pp. 730-742 ◽  
Author(s):  
Fabrice Rossi ◽  
Nathalie Villa
Keyword(s):  
Support Vector Machine ◽  
Functional Data ◽  
Data Classification ◽  
Support Vector
Sign in / Sign up

Export Citation Format

Share Document