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

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

The two-volume set LNCS 6593 and 6594 constitutes the refereed complaints of the tenth foreign convention on Adaptive and ordinary Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers awarded have been rigorously reviewed and chosen from a complete of one hundred forty four submissions. the second one quantity comprises forty-one papers equipped in topical sections on trend reputation and studying, tender computing, platforms idea, 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

Example text

R = A1 XOR A2 XOR A3 ), dRand (R is chosen at random). First, we investigated if the generated contributions reflect what the model learns. We evaluated the models with the relative root mean squared error (RRMSE). , ϕn ) and the vector generated when using optimal predictions instead of f . Table 1 shows the results for the described experiment. Some models perform better and some data sets are more difficult. Regardless, explanation quality and model performance are highly correlated. Correlation is not applicable to dRand.

2. Data sets used in experiments. The best clustering results of GSOM are displayed using different shapes or colors of markers. PCA projection is used to visualize Hepta, Iris, Wine, and LetterABC data. 1 Data Sets A short description of the used data sets is given as follows: a) Data set Giant consists of 862 2-D points and has two clusters: one small spherical cluster on the right side with 10 points and one huge spherical cluster with 852 points on the left side. A much greater density of the leftmost cluster, compared to the other one, is a difficulty here, leading algorithms to split the giant instead of finding the dwarf.

2 reflects any influence the feature might have on the prediction. However, in practice it is often impossible to calculate the Δ-terms due to the time complexity. Even if we could, we still face the exponential time complexity of computing 1 , for all x ∈ A. For any ϕi (x). In [8] this is resolved by assuming that p(x) = |A| given feature space this assumption limits the choice of p to a single possibility. The distribution of values plays an important part in how people intuitively explain events.

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