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

ISBN-10: 3642202829

ISBN-13: 9783642202827

The two-volume set LNCS 6593 and 6594 constitutes the refereed court cases of the tenth foreign convention on Adaptive and usual 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 a hundred and forty four submissions. the second one quantity contains forty-one papers equipped in topical sections on trend acceptance and studying, delicate computing, platforms conception, help 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

Example text

We can conclude it overfits. Fig. 5. M5P successfully models dDisj and correctly predicts R = 1. The visualization shows that a single feature is responsible for the prediction, while the other two have the opposite effect. Fig. 6. The neural network successfully models dXorBin and correctly predicts this instance. The explanation reveals that the first three features are important and all three contribute towards 1. Figure 2 is a visualization of the global importance of features for our illustrative data set testA.

While this reduces the time complexity, it, in some cases, does not result in a change that reveals the true importance of a feature. Observe how the value of the expression 1 ∨1 does not change if we change either of the 1’s to 0. Both must be changed at the same time to achieve a change. A recently published paper introduces FIRM, a method for computing the importance of features for a given model [9]. For each feature the method observes the variance of the conditional expected output of the model, across all possible values of that feature (conditional to the given value of the feature).

The devices chosen for the experiments were: a microwave, a coffee machine, a toaster, an incandescent lamp and two LCD’s (from the same manufacturer but different models). In order to proceed with StS identification, Theorem 1 from Section 3 was implemented obtaining a recognition algorithm for processing the collected signals. For each one of the different appliances is possible to identify three steady 1 For the coffee machine, the running time is less than a minute corresponding to the time needed for an expresso.

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Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II by Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)

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