A novel hybrid PSO- and GS-based hyperparameter optimization algorithm for support vector regression

Author:

Açıkkar MustafaORCID,Altunkol Yunus

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference43 articles.

1. Kuhn M, Johnson K (2013) Applied predictive modeling. Springer, New York, pp 61–65. https://doi.org/10.1007/978-1-4614-6849-3

2. Vapnik V (1995) The nature of statistical learning theory. Springer, New York. https://doi.org/10.1007/978-1-4757-2440-0

3. Ben-Hur A, Ong CS, Sonnenburg S, Schölkopf B, Rätsch G (2008) Support vector machines and kernels for computational biology. PLOS Comput Biol 4(10):1–10. https://doi.org/10.1371/journal.pcbi.1000173

4. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2007) Numerical recipes: the art of scientific computing, 3rd edn. Cambridge University Press, New York. . Chap. 16.5 Support Vector Machines

5. Drucker H, Burges C, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. Adv Neural Inform Process Syst 28:779–784

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