Download Adaptive and Natural Computing Algorithms: 10th by Ivan Bratko (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à PDF

By Ivan Bratko (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 normal 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 a hundred and forty four submissions. the 1st quantity contains forty two papers and a plenary lecture and is geared up in topical sections on neural networks and evolutionary computation.

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Read or Download Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part I PDF

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

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For each learning example that is left out in the current iteration, we compute the prediction and all the reliability estimates. The performance of reliability estimators is measured by computing the Spearman’s rank correlation coefficient between each set of reliability estimates and the real prediction error e(x) = 1 − fy (x). We also compute the Spearman’s coefficients for the reference function (Eq. 2) and our error function (Eq. 1) as control. Next, we compare the coefficient of each estimator with the coefficient of the reference function.

If the Z-test confirms that the two coefficients are Table 1. Brief summary of the testing data sets dataset housevotes wine parkinsons zoo tic-tac-toe postoperative monks-3 irisset glass hungarian ecoli heart haberman flag wdbc breast-cancer sonar hepatitis lungcancer #instances 435 178 195 101 958 90 432 150 214 294 336 303 306 194 569 369 111 155 32 #discrete 16 0 0 16 8 7 5 0 0 7 0 7 0 18 0 0 0 13 56 #continuous 0 13 22 0 0 1 0 4 9 6 7 6 3 10 30 9 60 6 0 Evaluating Reliability of Single Classifications of Neural Networks 27 significantly different, we measure whether the coefficient of the reliability estimator is greater (better) or smaller (worse) than that of the reference function.

To describe a relationship between RKHSs induced by two different scalings, K a and K b , of the same kernel K we apply the spectral theorm to the operator TK := JK LK obtained by composing LK with JK . The next theorem from [17] summarizes some properties of eigenfunctions and eigenvalues of these operators. Theorem 3. Let X ⊆ Rd be measurable, μ a σ-finite measure on X, K : X × X → R symmetric positive semidefinite with X K(x, x)dμ(x) < ∞. Then (i) TK : (L2μ (X), . L2μ ) → (L2μ (X), . L2μ ) is compact, self-adjoint, and positive; (ii) there exists at most countable orthonormal family{ψj | j ∈ I}in(L2μ (X), .

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