Removing the Black-Box from Machine Learning

Author:

Kuri-Morales Angel Fernando

Publisher

Springer Nature Switzerland

Reference19 articles.

1. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D., McClelland, J., the PDP research group. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1: Foundations. MIT Press (1986)

2. Haykin, S.: Neural Networks and Learning Machines, 3rd edn, Chap. 4, Multilayer Perceptrons. Prentice Hall (2009). ISBN-13: 978–0–13–147139–9

3. Powell, M.J.D.: The theory of radial basis functions. In:. Light, W (ed.) Advances in Numerical Analysis II: Wavelets, Subdivision, and Radial Basis Functions. King Fahd University of Petroleum & Minerals (1992)

4. Haykin, S.: Chap. 5, Kernel Methods and Radial Basis Functions (2009)

5. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20 (1995). http://www.springerlink.com/content/k238jx04hm87j80g/

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