Download Artificial Intelligence and Soft Computing: 11th by Yalemisew M. Abgaz, Muhammad Javed, Claus Pahl (auth.), PDF

By Yalemisew M. Abgaz, Muhammad Javed, Claus Pahl (auth.), Leszek Rutkowski, Marcin Korytkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)

ISBN-10: 3642293492

ISBN-13: 9783642293498

The two-volume set LNAI 7267 and 7268 (together with LNCS 7269 ) constitutes the refereed court cases of the eleventh overseas convention on synthetic Intelligence and smooth Computing, ICAISC 2012, held in Zakopane, Poland in April/ may perhaps 2012. The 212 revised complete papers awarded have been rigorously reviewed and chosen from 483 submissions. The papers are geared up in topical sections on neural networks and their functions, laptop imaginative and prescient, picture and speech research, info mining, implementation, bioinformatics, biometrics and scientific purposes, concurrent parallel processing, agent structures, robotics and regulate, synthetic intelligence in modeling and simulation, a variety of difficulties od synthetic intelligence.

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Extra resources for Artificial Intelligence and Soft Computing: 11th International Conference, ICAISC 2012, Zakopane, Poland, April 29-May 3, 2012, Proceedings, Part II

Example text

B) Initialize a competence set for k-kth child as samples with cok > 0. (c) For each cluster j in true values, in order from the closest to the farthest, do: i. Check if adding cluster j would cause the training set to be over 95% of the parent set. If yes, stop this loop. (Eik − a) s a n 2 + ii. Calculate function H = · · , where Eik is nk n nk s the error of the parent estimator on examples with cok > 0, n and nk are the numbers of samples in the parent training set and with cok > 0, respectively, a is the sum of the parent estimator errors on examples with ctl > 0 for all such l that cluster l has been added to the training set (including cluster j), s is the size of the current training set, including cluster j.

Visual Similarity Measuring. To represent visual layout and structure of a page, we use the results of the visual block classification. These results include information about position, visual properties and class of each individual visual block. This is used to create a vector of visual properties containing attributes that characterize overall layout of a page. Here is the list of properties used: – Font size, font name and color of the main heading - the main heading is discovered as a block of class “Heading” with the highest font size.

Abgaz, M. Javed, and C. Pahl The solution applied in other domain (university administration) shows a fairly similar result to the results in table 1. It identifies all the dependent entities that are manually identified and more axioms than the manual method. This shows that the dependency analysis is adequate and transferable to other similar domains. The algorithm gives us a complete list of all the dependent entities that are identified manually. This makes it complete and guarantees to return all dependent entities.

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