Adaptive and Natural Computing Algorithms: 10th by Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič,

By Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)

The two-volume set LNCS 6593 and 6594 constitutes the refereed lawsuits of the tenth foreign convention on Adaptive and normal Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers offered have been conscientiously reviewed and chosen from a complete of one hundred forty four submissions. the second one quantity comprises forty-one papers prepared in topical sections on trend acceptance and studying, delicate computing, structures conception, aid vector machines, and bioinformatics.

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Extra resources for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II

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It can explain both the model and its predictions. Results across different regression models and data sets confirmed that the method’s explanations reflect what the models learn, even in cases where existing general explanation methods would fail. The examples presented throughout the paper illustrate that the method is a useful tool for visualizing models, comparing them, and identifying potential errors. With emphasis on the theoretical properties and the method’s usefulness, less attention was given to measuring and optimizing running times.

At each iteration of an algorithm, every point x in the remaining set of points denoted with P is considered once. Then we have to choose another point y and move both of them according to Eq. 2. As both points are actually BMUs, taken from the SOM, point y can be selected in two ways: either from the neighbors of x, if any of them exist, or as a random point from the set P , not equal to the point x. With probability 1 − p, one of the existing neighbors of the x is randomly chosen and with probability p a random point from P is selected, where p is a parameter of an algorithm.

D. d. d. 9 Acc. 9 Evaluation Results For each one of the tests one-against-all (SVM and 5-NN) the F-scores and mean accuracies are illustrated in Table 1. Towards a global evaluation, the macroaverages (mean values of the F-scores), micro-averages and mean accuracies are also presented. As it can be observed, the one-against-all approaches performance is quite effective. In average, we have an accuracy around 96% for the Linear SVM, 97% for the RBF SVM and 99% for the 5-NN. Rather, the multi-class method presents very low accuracy values: around 40%.

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