By Alan Murray (auth.), Alan F. Murray (eds.)
Applications of Neural Networks supplies an in depth description of thirteen functional purposes of neural networks, chosen as the initiatives played by way of the neural networks are genuine and important. The contributions are from major researchers in neural networks and, as a complete, offer a balanced assurance throughout various program parts and algorithms. The publication is split into 3 sections. part A is an advent to neural networks for nonspecialists. part B appears to be like at examples of functions utilizing `Supervised Training'. part C provides a couple of examples of `Unsupervised Training'.
For neural community lovers and , open-minded sceptics. The booklet leads the latter throughout the basics right into a convincing and sundry sequence of neural good fortune tales -- defined conscientiously and in truth with out over-claiming. Applications of Neural Networks is key studying for all researchers and architects who're tasked with utilizing neural networks in genuine lifestyles functions.
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Extra info for Applications of Neural Networks
Anderson, "An alternative to backpropagation : A simple rule for synaptic modification for neural net training and memory", Internal Report, Univ. , 1989. 21. J. Werbos, "A menu for designs of reinforcement learning over time", in Neural networks for control, ed. S. Sutton, and P. J. , 1990. 22. G. Barto and P. Anandan, "Pattern Learning Stochastic Learning Automata", IEEE Trans Systems, Man and Cybernetics, vol. 15, pp. 360-375, 1985. 23. T. Prescott and J. Mayhew, "Obstacle Avoidance through Reinforcement Learning", Neural Information Processing Systems (NIPS) Conference, pp.
However, many more parameter sets could have been tried out. Table 1 gives details for the best MLPs when trained on a sixty image database. The database was made at BT Laboratories  and training was executed on a 100 Mips Apollo DN10000. 3 Avg. 89 It is interesting to compare these figures with those from an earlier, less rigorous, phase of training. The results for the test set are shown in Table 2. The number of rejections (failure to detect a feature vector) must be read in conjunction with the figure 48 for mean search area, which is a measure of the number of false positives.
Feature rejection rate, RejT, may be similarly defined. W z'dt h image. H ezg . 100 ' ' ' ' ------------------------------------------------------------------ - - - Feature points retained -Average search area /Image area -------------------------------------- .. j:>. _ % 10 100 0 10 : -- ------- - ~ - - - -- 20 -------- -- - 30 _. •••••. ···········••••••·•••·••••••• ••· ' ' . j:>. 1 10 100 t ~ 0 20 Average search region area 1 Total image area Figure 4 graph3 Training lime In epochs 40 60 ~ - - - Feature points retained - 80 ----- --- :.
Applications of Neural Networks by Alan Murray (auth.), Alan F. Murray (eds.)