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 court cases of the tenth overseas convention on Adaptive and traditional Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers provided have been conscientiously reviewed and chosen from a complete of one hundred forty four submissions. the second one quantity contains forty-one papers prepared in topical sections on trend acceptance and studying, tender computing, structures thought, aid vector machines, and bioinformatics.

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For energy monitoring, health care or home automation, concepts like Smart Grids or in-Home Activity Tracking are a recent and important trend. In that context, an accurate and inconspicuous identification and monitoring of electrical appliances consumptions are required. Moreover, such monitoring system should be inconspicuous. Currently, the available solutions for load consumption monitoring are smart meters and individual meters. The first ones supply aggregated consumption information without identifying which devices are on.

Order of seconds) for data sets with up to a few dozen features (already shown in [8]). A more rigorous analysis of the limits of the method wrt the number of features it can handle for a given type of model is delegated to further work. 3 Experimental Verification We have shown that the method is theoretically well founded and has several desirable properties. But how well does it translate into practice. 1. Due length limits, we omit an in-depth analysis of running times in favor of showing more examples.

Therefore, it can be applied to both nominal and numeric features. Observe the same model’s prediction for the same instance, but from data set testB where the teacher tests the students a vast majority of time Visualizing and Explaining Black-Box Regression Models 25 Algorithm 1. Approximating ϕi (x), the importance of the i-th feature’s value for instance x and model f . Take m samples. ϕi (x) ← 0 for k = 1 to m do select (at random) permutation O ∈ π(n) and instance y ∈ A take their values from y take their values from x x1 ← feat.

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