By Aristidis Likas, Konstantinos Blekas, Dimitris Kalles
This publication constitutes the court cases of the eighth Hellenic convention on synthetic Intelligence, SETN 2014, held in Ioannina, Greece, in might 2014. There are 34 general papers out of 60 submissions, additionally five submissions have been accredited as brief papers and 15 papers have been approved for 4 distinctive classes. 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: thought and perform; computational intelligence strategies for bio sign research and review; online game man made intelligence; multimodal advice structures and their functions to tourism.
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Extra resources for Artificial Intelligence: Methods and Applications: 8th Hellenic Conference on AI, SETN 2014, Ioannina, Greece, May 15-17, 2014. Proceedings
PClass address technical flaws of GENEFIS-class, which are detailed in the sequel as follows: • GENEFIS-class endures a rule growing demerit, where the novelty of streaming data is extracted with an unrealistic assumption of uniformly distributed streaming data of DS method and excludes spatial and temporal proximities of the datum with other data points. Consequently, it is incompetent to posit the rule focalpoints in the strategic zone of the input space, while being vulnerable with noisy streaming data and imbalanced training samples.
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34, 484–498 (2004) 5. : Multivariable Gaussian Evolving Fuzzy Modeling System. IEEE Transactions on Fuzzy Systems 19(1), 91–104 (2011) 6. : PANFIS: A Novel Incremental Learning. IEEE Transactions on Neural Networks and Learning Systems (2013) (online and in press) 7. : Simpl_eTS: A simplified method for learning evolving TakagiSugeno fuzzy models. In: IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1068–1073 (2005) 8.
By extension, the MIMO architecture is presumed to deal with the class overlapping problem more satisfactorily than the MM architecture as it is invigorated by an independent rule consequent per a class label. , K A more accurate classification boundary can be concocted in comparison with the MM architecture in the region in which the classes overlap, as the standalone decision boundary per class can be crafted, thus leading to more reliable classification results. In this paper, we exclude to exploit the zero-order classifier’s architecture, which does not likely work out to incur dependable classification results in many cases, as it foresees the class label rather than the classification surface.