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 usual Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers provided have been rigorously reviewed and chosen from a complete of a hundred and forty four submissions. the second one quantity comprises forty-one papers geared up in topical sections on trend popularity and studying, delicate computing, structures conception, help vector machines, and bioinformatics.

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

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B. Figueiredo, A. de Almeida, and B. Ribeiro A non-intrusive load monitoring system (NILM) fulfills all the requirements imposed by the Smart Grids and in-Home Activity Tracking challenges at virtually no cost. NILM is a viable solution for monitoring individual electrical loads: a single device is used to monitor the electrical system and to identify the electric load related to each appliance, without increasing the marginal cost of electricity or needing extra sub-measurements. Nevertheless, only with the present low-cost sensing devices, its full potential could be achieved.

The explanation reveals which features contribute towards/against compressive strength. 4 Conclusion The proposed explanation method is simple to implement and can be applied to any regression model. 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.

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. Each grey/black point pair is obtained by running Algorithm 2. The mean of ψi,j samples (black points) reveals the magnitude and direction of the value’s average influence.

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