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

By Aristidis Likas, Konstantinos Blekas, Dimitris Kalles

ISBN-10: 3319070630

ISBN-13: 9783319070636

ISBN-10: 3319070649

ISBN-13: 9783319070643

This ebook constitutes the complaints of the eighth Hellenic convention on man made Intelligence, SETN 2014, held in Ioannina, Greece, in might 2014. There are 34 common papers out of 60 submissions, furthermore five submissions have been permitted as brief papers and 15 papers have been accredited for 4 certain periods. They care for emergent themes of man-made intelligence and are available from the SETN major convention in addition to from the subsequent exact classes on motion languages: conception and perform; computational intelligence ideas for bio sign research and overview; video game man made intelligence; multimodal suggestion platforms and their functions to tourism.

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

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Denoting by Xre the set of the initial cluster representatives, ηj ’s are initialized as follows. First, the distance of each θj ∈ Xre from its closest θs ∈ Xre − {θj }, denoted by dmin (θ j ), is determined and then ηj is set to d (θ j )/2 ηj = min − log β , where β ∈ (0, 1) is an appropriately chosen parameter (see Initialization of ηj ’s part in the description of the SAPCM algorithm). 5]. The experiments showed also that β depends on how densely the natural clusters are located; smaller values of β are more appropriate for sparsely located clusters, while larger values of β are more appropriate for more densely located clusters.

The pruned fuzzy rule should be reactivated, rather than evolving the datum as an supplementary fuzzy rule, which is resolved as follow: C R +1 = C i* ,  R +1 −1 =  i* −1 , ΨR +1 = ϖI , Ω R +1 = Ω winner (23) It is worth-stressing that the rules pruned in the past training episodes are solely exploited to enumerate their density. Even so, they are eradicated in any learning mechanisms or inference mechanisms, thereby still being able to guarantee the abatement of the computational complexity.

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. In this case η is set initially equal to djslope which, in this case, turns out to be significantly larger than dmax . Experiments show that dmax is a small value for η in this case, while djslope leads to better cluster estimations.

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Artificial Intelligence: Methods and Applications: 8th Hellenic Conference on AI, SETN 2014, Ioannina, Greece, May 15-17, 2014. Proceedings by Aristidis Likas, Konstantinos Blekas, Dimitris Kalles


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