A Novel Hybrid GMM/SVM Architecture for Protein Secondary Structure Prediction

Authors:
Emad Bahrami Samani;M. Mehdi Homayounpour;Hong Gu
Affiliations:
Computer Engineering and IT Department, Amirkabir University of Technology, Tehran, Iran;Computer Engineering and IT Department, Amirkabir University of Technology, Tehran, Iran;Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, Canada
Venue:
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Year:
2007

Citing 4
Cited 0

Bioinformatics: the machine learning approach

Bioinformatics: the machine learning approach
GMM-based SVM for face recognition

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
A New Hybrid GMM/SVM for Speaker Verification

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Working Set Selection Using Second Order Information for Training Support Vector Machines

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

The problem of secondary structure prediction can be formulated as a pattern classification problem and methods from statistics and machine learning are suitable. This paper proposes a new combination approach between Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) by typical sample extraction based on a UBM/GMM system for SVM in protein secondary structure prediction. Our hybrid model achieved a good performance of three-state overall per residue accuracyQ3= 77.6%which is comparable to the best techniques available.