Programs
- Amrita’s Fetal Cardiology learning ( AFCL) Module – Fetal Heart Hybrid - Fellowship
- B.Tech. in Artificial Intelligence (AI) and Data Science (Medical Engineering) - Undergraduate
Publication Type : Conference Paper
Publisher : 2009 International Conference on Advances in Recent Technologies in Communication and Computing
Source : 2009 International Conference on Advances in Recent Technologies in Communication and Computing (2009)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2009
Abstract : This paper proposes a research work done in search of best-supervised learning algorithm and the best kernel for Hyperspectral Image classification. In this work, we find that SVM outperforms other supervised algorithms. Many kernels are utilized in support vector machines for classification. Among them Linear, Polynomial and RBF kernels are analysed and the kernel that best suits for the application is determined. Cuprite (Nevada, USA) is the Hyperspectral image used in this paper.
Cite this Research Publication : V. Joevivek, Hemalatha, T., and Dr. Soman K. P., “Determining an Efficient Supervised Classification Method for Hyperspectral Image”, in 2009 International Conference on Advances in Recent Technologies in Communication and Computing, 2009.