By Enrique Alba, Christian Blum, Pedro Asasi, Coromoto Leon, Juan Antonio Gomez
Fixing complicated difficulties addresses actual difficulties and the fashionable optimization recommendations used to unravel them. Thorough examples illustrate the purposes themselves, in addition to the particular functionality of the algorithms. software components contain computing device technology, engineering, transportation, telecommunications, and bioinformatics, making the booklet in particular invaluable to practitioners in these components.
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Extra resources for Optimization Techniques for Solving Complex Problems (Wiley Series on Parallel and Distributed Computing)
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However, the idea of selecting the k nearest patterns might not be the most appropriate, mainly because the network will always be trained with the same number of training examples. In this work we present a lazy learning approach for artificial neural networks. This lazy learning method recognizes from the entire training data set the patterns most similar to each new query to be predicted. The most similar patterns are determined by using weighting kernel functions, which assign high weights to training patterns close (in terms of Euclidean distance) to the new query instance received.
A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review , 11:273–314, 1997. 4. V. Dasarathy, ed. Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos, CA, 1991. 5. L. Bottou and V. Vapnik. Local learning algorithms. Neural Computation, 4(6):888–900, 1992. 6. J. E. Moody and C. J. Darken. Fast learning in networks of locally-tuned processing units. Neural Computation, 1:281–294, 1989.
3 Comparative Analysis with Other Classical Techniques To compare the proposed method with classical techniques related to our approach, we have performed two sets of experiments over the domains studied, in one case using eager RBNN and in the other, well-known lazy methods. In the first set we have used different RBNN architectures trained with all the available training patterns to make the predictions on the test sets; in the second set of experiments, we have applied to the domains studied the following classical lazy methods: k-nearest neighbor, weighted k-nearest neighbor, and weighted local regression.