By Adrian Horzyk (auth.), Mikko Kolehmainen, Pekka Toivanen, Bartlomiej Beliczynski (eds.)
This e-book constitutes the completely refereed post-proceedings of the ninth foreign convention on Adaptive and typical Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009.
The sixty three revised complete papers provided have been conscientiously reviewed and chosen from a complete of 112 submissions. The papers are equipped in topical sections on impartial networks, evolutionary computation, studying, delicate computing, bioinformatics in addition to applications.
Read or Download Adaptive and Natural Computing Algorithms: 9th International Conference, ICANNGA 2009, Kuopio, Finland, April 23-25, 2009, Revised Selected Papers PDF
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Additional info for Adaptive and Natural Computing Algorithms: 9th International Conference, ICANNGA 2009, Kuopio, Finland, April 23-25, 2009, Revised Selected Papers
In other words, the suitability of imputation strategy is evaluated in terms of its impact on the prediction or classiﬁcation process that is performed using the data set. The framework combines genetic algorithms and multilayer perceptrons. Genetic algorithms have been used to evolve method vectors representing imputation strategies. Multilayer perceptrons (MLP) have been applied to represent classiﬁcation models used in test cases. The error rate of classiﬁcation performed on the data sets imputed using a strategy, provides basis for the ﬁtness function.
In the upper bounds in Theorem 1 (i)-(iii), the functions ξ(d) are of the forms ξ(d) = sd rd , ξ(d) = h∗d sd rd , and ξ(d) = d1/2 sd rd , resp. , grow polynomially with d increasing. Note that sd and h∗d,Θ are determined by the choice of Gd , but rd can be chosen in such a way that ξ(d) is a polynomial. 4 Tractability for Gaussian Radial-Basis-Function Networks In this section, we investigate tractability of approximation by Gaussian radialbasis function (RBF) networks. Let γd,b : Rd → R denote the d-dimensional Gaussian function of width b, given by 2 γd,b (x) = e−b x .
22–31, 2009. c Springer-Verlag Berlin Heidelberg 2009 Handling Incomplete Data Using Evolution of Imputation Methods 23 that a value is missing may depend on the value itself. The method proposed in this paper does not directly require the data to ﬁt to one speciﬁc type. However, as method vectors use imputation methods, such requirements may be imposed by the latter. There are three general approaches in face of incompleteness. First of all, an attempt can be made to acquire and ﬁll in the missing data.