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 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 contains forty-one papers geared up in topical sections on trend reputation and studying, tender computing, platforms thought, help vector machines, and bioinformatics.

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The methods time complexity is O(n · T (f (x)). For most regression algorithms T (f (x)) is O(n), which implies quadratic time complexity. Our purpose is to show that the method is a wellfounded and useful tool, which can be used to generate explanations in real-time Visualizing and Explaining Black-Box Regression Models Fig. 7. SVM provides the best fit for dPoly. Subsequently, the contributions closely match the actual concepts. 27 Fig. 8. The visualization shows us that MP learned some but not all of the concepts behind the dPoly data set.

The teacher can immediately pass the student or may opt to test the student with additional questions in which case it comes down to the student’s knowledge to determine whether the student will pass. The model’s task is to predict the student’s chances of success. The binary feature teacher describes the teachers action. , and 5 means certain success. In testA all combinations of pairs of values the two features are equally probable. The explanation in Figure 1 is consistent with our intuition and helps us understand the model’s prediction.

706, respectively. 30 ˇ E. Strumbelj and I. Kononenko Figures 9 and 10 are visualizations for LR and MP. These are used to reveal the overall importance of individual features and their contribution to the model’s predictions. When interested in a specific prediction, we observe the corresponding instance explanation. For example, Figure 11 is an instance explanation for MP’s prediction for a particular concrete mixture. MP’s prediction for is close to the actual concrete compressive strength, while LR overestimates the compressive strength for this instance and predicts 60 MPa.

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