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 accuracy Q 3 = 77.6% which is comparable to the best techniques available.
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Samani, E.B., Homayounpour, M.M., Gu, H. (2007). A Novel Hybrid GMM/SVM Architecture for Protein Secondary Structure Prediction. In: Masulli, F., Mitra, S., Pasi, G. (eds) Applications of Fuzzy Sets Theory. WILF 2007. Lecture Notes in Computer Science(), vol 4578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73400-0_62
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DOI: https://doi.org/10.1007/978-3-540-73400-0_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73399-7
Online ISBN: 978-3-540-73400-0
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