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Quantifying Generalization in Linearly Weighted Neural Networks

Journal article published in 1995 by Sean B. Holden, Martin Anthony
This paper was not found in any repository; the policy of its publisher is unknown or unclear.
This paper was not found in any repository; the policy of its publisher is unknown or unclear.

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Abstract

2.1 The VC dimension 5 2.2 Using the growth function and the VC dimension to analyse generalization 8 2.3 VC dimension and computational learning theory 9 2.3.1 Standard PAC learning 9 2.3.2 Extended PAC learning 10 3.1 Interpolation and Micchelli's theorems 12 3.2 Networks with xed centres 16 3.3 Networks with variable centres 17 4.1 Threshold order of a boolean function 20 4.2 Further notations and denitions 21 4.3 VC dimension and independence of basis functions 22 4.4 VC dimension of PDFs 25 2 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 1 Linearly weighted neural networks 3 2...