Radial Basis Function Networks 2: New Advances in Design by J. Ghosh, A. Nag (auth.), Dr. Robert J. Howlett, Professor

By J. Ghosh, A. Nag (auth.), Dr. Robert J. Howlett, Professor Lakhmi C. Jain (eds.)

The Radial foundation functionality (RBF) neural community has won in acceptance over contemporary years due to its swift education and its fascinating houses in class and sensible approximation purposes. RBF community study has eager about more advantageous education algorithms and diversifications at the uncomplicated structure to enhance the functionality of the community. additionally, the RBF community is proving to be a important instrument in a various diversity of software components, for instance, robotics, biomedical engineering, and the monetary region. the 2 volumes supply a accomplished survey of the most recent advancements during this quarter. Volume 2 encompasses a wide variety of purposes within the laboratory and case reports describing present business use. either volumes will turn out super priceless to practitioners within the box, engineers, reserachers, scholars and technically entire managers.

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And Linder, T. , and Petsche, T. ), Advances in Neural Information Processing Systems, vol. 9, p. 197. , and Lugosi, G. (1996), "Nonparametric estimation and classification using radial basis function nets and empirical risk minimization," IEEE Transactions on Neural Networks, vol. 7, no. 2, pp. 475-487, March. [27] Kadirkamanathan, V. and Niranjan, M. (1993), "A function estimation approach to sequential learning with neural networks," Neural Computation, vol. 5, pp. 954-975. A. P. ), Approximation Theory, Spline Functions and Applications, pp.

6, no. 3, pp. 469-505. [34] Moody, J. J. (1989), "Fast learning in networks of locally-tuned processing units," Neural Computation, vol. 1, no. 2, pp. 281-294. 34 Chapter 1 [35] Megdassy, P. (1961), Decomposition of superposition of distributed functions, Hungarian Academy of Sciences, Budapest. A. (1986), "Interpolation of scattered data: distance matrices and conditionally positive definite functions," Constructive Approximation, vol. 2, pp. 11-22. [37] Molina, C. and Niranjan, M. (1996), "Pruning with replacement on limited resource allocating networks by F-projections," Neural Computation, vol.

1 along its diagonal. 2 Pruning and Growing RBFNs The concept of projection matrix and the associated geometrical interpretation of SSE provides an appealing way of growing an RBFN using forward selection. 1 Forward Selection One is given an initial network configuration and a candidate pool of basis functions, typically Gaussians centered at the training data points. · This process of adding hidden units and increasing the model complexity is continued till some criterion such as GCV stops decreasing.

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