By Angel Kuri-Morales, Edwin Aldana-Bobadilla (auth.), Félix Castro, Alexander Gelbukh, Miguel González (eds.)
The two-volume set LNAI 8265 and LNAI 8266 constitutes the complaints of the twelfth Mexican foreign convention on synthetic Intelligence, MICAI 2013, held in Mexico urban, Mexico, in November 2013. the full of eighty five papers offered in those complaints have been conscientiously reviewed and chosen from 284 submissions. the 1st quantity bargains with advances in synthetic intelligence and its functions and is dependent within the following 5 sections: common sense and reasoning; knowledge-based structures and multi-agent structures; normal language processing; laptop translation and bioinformatics and clinical purposes. the second one quantity offers with advances in smooth computing and its functions and is based within the following 8 sections: evolutionary and nature-inspired metaheuristic algorithms; neural networks and hybrid clever structures; fuzzy platforms; desktop studying and development acceptance; facts mining; desktop imaginative and prescient and photograph processing; robotics, making plans and scheduling and emotion detection, sentiment research and opinion mining.
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Additional info for Advances in Soft Computing and Its Applications: 12th Mexican International Conference on Artificial Intelligence, MICAI 2013, Mexico City, Mexico, November 24-30, 2013, Proceedings, Part II
C m X m (5) Xi denotes a combination of the independent variables. That is, Xi = fi(v). According to the way these combinations are defined one may obtain different approximants. Now, from the universal approximation theorem , any function of n variables may be approximated with at most k ( 2 j − 1 + n)! 2i − 1 + k j =1 ( 2 j − 1)! n! j =1 ( 2 j − 1)! n! (6) terms of degree k. The expression of T yields numbers of the order of 1012 even for small n. Obviously it makes no sense to try to approximate any function with a polynomial of these many terms.
The probability that Ai reaches some minimum value (which we denote by κ ) is unknown. These κ will vary for every problem and will distribute with mean μ and standard deviation σ which are also unknown. We shall approximate these values by sampling U. It is, therefore, of utmost importance that we select sample S adequately; both in its nature and its size. Typically, the size of S is determined from assumptions (directly or indirectly) depending on the form of the population’s ( κ ’s distribution).
The feature of conﬁdence was added to the GA, even the information provided is not used at the moment. Figure 1 suggests the existence of negative correlation between error and population mean-conﬁdence. Therefore, conﬁdence can be used someway to lead the algorithm into better performance. It could be hypothesized, as correlation becomes higher, the performance of a conﬁdence-based GA will improve. In the ideal case, where real evaluations could be obtained without computation, inheritance-based GA would behave as a GA with variable population.