New PDF release: Graph-Theoretic Concepts in Computer Science: 21st

By Evangelos Kranakis, Danny Krizanc (auth.), Manfred Nagl (eds.)

ISBN-10: 3540606181

ISBN-13: 9783540606185

This publication constitutes the refereed court cases of the twenty first overseas Workshop on Graph-Theoretic suggestions in desktop technological know-how, WG '95, held in Aachen, Germany, in June 1995. The WG workshop sequence contributes to integration in machine technology by means of making use of graph theoretical options in quite a few components in addition to by way of taking on difficulties from useful functions and treating them theoretically.
The publication offers 30 rigorously refereed revised papers chosen from fifty two submissions and displays present actions within the box of laptop technology orientated graph idea, its computational points and its application.

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Extra info for Graph-Theoretic Concepts in Computer Science: 21st International Workshop, WG '95 Aachen, Germany, June 20–22, 1995 Proceedings

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12000 of these are used as learning set, and others are used in test. 85, respectively. 007895. The error is computed based on mean square error (MSE) [15]. Fig. 2. 000 iterations. After the off-line neural network training is finished, the neural network, which works online, is coded with obtained synaptic weights as seen in Figure 2. The neural network includes 20 neurons in the hidden layer, and it has been tried to obtain the neural network controller with the least number of perceptron in the hidden layer.

However, it has a complex computational structure and needs too much computation time. On the other hand, a neural network can give results rather quickly. In the simulation studies, it is observed that the training process is too much important for the accuracy of the obtained ANN control system. Generalization is also made through the reproduction of a situation that was absent from the training set. On the other hand, the system is more successful for the trajectory, which is in training set than one, which is not in training set.

ISCIS 2004, LNCS 3280, pp. 11–20, 2004. © Springer-Verlag Berlin Heidelberg 2004 12 et al. recognized and classified by different system units. In this paper, it was aimed to reach higher classification accuracy by assigning weights to important attributes in classification. This was done with some modifications to affinity measures of AISs and then a new system named AWAIS (Attribute Weighted Artificial Immune System) has come into existence. AiNet, one of the algorithms in which shape-space representation is used, is both a network-based and population-based AIS algorithm to be used in data mining and classification applications [4].

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Graph-Theoretic Concepts in Computer Science: 21st International Workshop, WG '95 Aachen, Germany, June 20–22, 1995 Proceedings by Evangelos Kranakis, Danny Krizanc (auth.), Manfred Nagl (eds.)

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