Margin Pruning Adaboost Classification with Nature Inspired Optimization for Alcoholic EEG Classification

Author:

Rajaguru Harikumar,Prabhakar Sunil Kumar

Publisher

Springer International Publishing

Reference9 articles.

1. Zhang, L., Begleiter, H., Porjesz, B., Litke, A.: Electrophysiological evidence of memory impairment in alcoholic patients. Biol. Psychiat. 42, 1157–1171 (1997)

2. Kousarrizi, M.R.N., Ghanbari, A.A., Gharaviri, A., Teshnehlab, M., Aliyari, M.: Classification of alcoholics and non-alcoholics via EEG using SVM and neural networks. In: 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, pp. 1–4 (2009)

3. UCI KDD Database. https://archive.ics.uci.edu/ml/datasets/eeg+database

4. Smola, J., Scholkopf, B.: A tutorial on support vector regression. J. Stat. Comput. 1, 199–222 (2002)

5. Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods: Support Vector Learning, chap. 12, pp. 185–208. MIT Press, Cambridge (1999)

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