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New PDF release: Access 2007

By John Hales

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Example text

Practically we do not introduce more than two hidden layers. This reflects general features of those layers. The first hidden layer extracts local features. Some neurons in this layer are used to partition the input space into regions, and other neurons learn the local features of those regions. The second hidden layer extracts global features. A neuron in the second layer combines the outputs of neurons in the first hidden layer operating on a particular region of the input space, and thereby learns the global features for that region.

B) . . . · · · . ....... ... Pattern -2- . . . . . " . ··· ............. . ... . .. ... ···· ......... . ..... ........ ··........ . Initial corrvpled 6 . . ···· ....... .... ... . .... ·· ...... .... . •..... . •..... ··· .... ... . . . A er 37 iterations Pollem -6- c) ........ ··· ........ ... •. •.. ··.... ...... .... ......... ···... . . ...... ·..... .. · ·........ Initial corrvpted 2 .... ... ···· .... ···· ...... .... ••....

Other approaches are related to random selection of centres or to the application of self-organizing selection associated with unsupervised learning, cf. 3. From among the methods which have been used for the estimation of the number of RBFs the LSM method combined with the standard Gram-Schmidt orthogonalization procedure is worth pointing out [2]. More about the problem mentioned above can be found in references given in the books [2,17]. The RBF neural network (RBFN) has many advantages in comparison with sigmoid type networks.

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Access 2007 by John Hales


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