Abstract
In this study, a support vector machine (SVM) classifies real world data of cytogenetic signals measured from fluorescence in-situ hybridization (FISH) images in order to diagnose genetic syndromes. The study implements the SVM structural risk minimization concept in searching for the optimal setting of the classifier kernel and parameters. We propose thresholding the distance of tested patterns from the SVM separating hyperplane as a way of rejecting a percentage of the miss-classified patterns thereby allowing reduction of the expected risk. Results show accurate performance of the SVM in classifying FISH signals in comparison to other state-of-the-art machine learning classifiers, indicating the potential of an SVM-based genetic diagnosis system.
Original language | English |
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Pages | 289-292 |
Number of pages | 4 |
State | Published - 1 Dec 2004 |
Event | 2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings - Tel-Aviv, Israel Duration: 6 Sep 2004 → 7 Sep 2004 |
Conference
Conference | 2004 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings |
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Country/Territory | Israel |
City | Tel-Aviv |
Period | 6/09/04 → 7/09/04 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Electronic, Optical and Magnetic Materials