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 foreign convention on Adaptive and common 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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Additional resources for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part I
05 (5% positive, 5% negative cases). From the sorted results, the estimators CNK and LCV stand apart from other estimators. In Fig. 1 we have seen best results for CNK, with other positive cases they represent 15%. For the estimator LCV from Fig. 2, the positive cases sum to 10%. 4 Conclusions We wanted to know if we can get new insight into the reliability assessments of single classiﬁcations and we made an evaluation of existing methods. We derived a reference function, which enables comparison between the reliability estimators and the model’s own predictions.
The reactor The nonlinear steady-state part of the system is described by functions shown in Fig. 3 (valves with saturation for which inverse functions do not exist). Fig. 3 also shows neural approximations of the steady-state part (two networks with K 1 = K 2 = 5 hidden nodes are used). The following MPC algorithms are compared: a) the classical MPC algorithm based on the linear model, b) the discussed MPC-NPAL algorithm based on the neural Wiener model and quadratic programming, c) the MPC-NO algorithm with on-line nonlinear optimisation, it uses the same neural Wiener model.
Strumbelj, and I. Kononenko Because the true conditional probabilities are inaccessible, for approximation we can substitute them with the model’s predicted probabilities. Substituting py (x) with fy (x) and further fy (x) with the prediction yˆ, we get a reference estimator of the expected error: Oref = 2(ˆ y − yˆ2 ) . (2) This reference reliability Eq. (2) has two desirable properties. First, we can compute it for every prediction, because yˆ is easily accessible. Second, in case of an optimal (or near-optimal) model, this reference becomes optimal as well (that is, equals the error function).