Download Artificial Intelligence: Methods and Applications: 8th by Aristidis Likas, Konstantinos Blekas, Dimitris Kalles PDF

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By Aristidis Likas, Konstantinos Blekas, Dimitris Kalles

This ebook constitutes the lawsuits of the eighth Hellenic convention on man made Intelligence, SETN 2014, held in Ioannina, Greece, in could 2014. There are 34 typical papers out of 60 submissions, furthermore five submissions have been authorised as brief papers and 15 papers have been permitted for 4 specified classes. They take care of emergent subject matters of man-made intelligence and are available from the SETN major convention in addition to from the next designated classes on motion languages: thought and perform; computational intelligence suggestions for bio sign research and evaluate; online game synthetic intelligence; multimodal suggestion structures and their purposes to tourism.

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Extra info for Artificial Intelligence: Methods and Applications: 8th Hellenic Conference on AI, SETN 2014, Ioannina, Greece, May 15-17, 2014. Proceedings

Example text

However, the k-means and the FCM differ from PCM algorithms in that the former two impose a clustering structure on the data set (that is they split the data set into the given number of clusters, independently of the fact that the data set may contain more or less physical clusters than that number), while the latter, in principle, leads the cluster representatives to regions that are “dense in data points”. Thus, in this case, the scenario where two or more cluster representatives are led to the same “dense in data” region in space, may arise.

This was articulated and proven mathematically in [28]. On the contrary, the new weight vector is designated as the rule consequent of the winning rule. The rule consequent of the winning rule can be expected to delineate an identical trend of the new rule, thus being able to attain the convergence more promptly. If the training observation cannot concur with the rule generation conditions, or the new knowledge conveys a marginal conflict with the existing ones, the rule premise adaptation is activated to refine the position and coverage of the existing rules as follows: C winner N =  win ( N ) − 1 =  win ( N − 1) −1 1−α + N win N −1 N win N −1 +1 C win N −1 + ( X N − C win N −1 ) N −1 +1 (  win ( N − 1) −1( X N − C win N − 1))(  win ( N − 1) −1( X N − C win N − 1))T 1−α 1 + α ( X N − C win N − 1)  win (old ) − 1( X N − Cwin N − 1)T α N win N = N win N −1 + 1 N −1 N win (17) (18) (19) where α = 1 ( N win + 1) .

For data sets whose points form well ini of each new separated clusters, dmax is, in general, a good estimate for ηnew cluster. In this case, since the initial estimates of the representatives are clusters points7 , dmax is a reasonable value for controlling the influence of a cluster around its representative. Note also, that in this case djslope is close to dmax . g. outliers), the algorithm is likely to choose some of them as initial estimates of cluster representatives. However, a small initial value of η for these representatives will make difficult their movement to dense in data regions.

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