Research on Classification of Fiber Intrusion Signal Based on Supported Vector Machines

[1]  R. J. Kuo,et al.  Evolutionary-based support vector machine , 2011, 2011 IEEE International Conference on Industrial Engineering and Engineering Management.

[2]  Yi Li,et al.  High Sensitive Micro-Displacement Sensor Based on M-Z Interferometer by a Bowknot Type Taper , 2014, IEEE Photonics Technology Letters.

[3]  Chin-Teng Lin,et al.  An automatic method for selecting the parameter of the RBF kernel function to support vector machines , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[4]  V. Dimri,et al.  Fractal analysis of aftershock sequence of the Bhuj earthquake : A wavelet-based approach , 2005 .

[5]  Stuart A. Kingsley,et al.  Distributed fiber optic acoustic sensor for leak detection , 1992, Other Conferences.

[6]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[7]  Vijander Singh,et al.  Optimal algorithm for ECG denoising using Discrete Wavelet Transforms , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[8]  Jens Sadowski,et al.  Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification , 2003, J. Chem. Inf. Comput. Sci..

[9]  Jerry Worsley,et al.  OptaSense: Fibre Optic Distributed Acoustic Sensing for Border Monitoring , 2012, 2012 European Intelligence and Security Informatics Conference.

[10]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.