By Aristidis Likas, Konstantinos Blekas, Dimitris Kalles
This e-book constitutes the lawsuits of the eighth Hellenic convention on man made Intelligence, SETN 2014, held in Ioannina, Greece, in could 2014. There are 34 usual papers out of 60 submissions, additionally five submissions have been accredited as brief papers and 15 papers have been permitted for 4 targeted classes. They care for emergent issues of synthetic intelligence and are available from the SETN major convention in addition to from the next unique classes on motion languages: idea and perform; computational intelligence ideas for bio sign research and evaluate; video game man made intelligence; multimodal advice structures and their functions to tourism.
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Additional resources for Artificial Intelligence: Methods and Applications: 8th Hellenic Conference on AI, SETN 2014, Ioannina, Greece, May 15-17, 2014. Proceedings
1 Introduction Clustering is a well-established data analysis method, where the aim is to locate the physical groups involved in the problem at hand (clusters) formed by a number of entities (usually each entity is represented by a set of measurements that constitute the corresponding feature vector). Various clustering philosophies have been proposed during the last ﬁve decades. Among them are the hard clustering philosophy, where each entity belongs exclusively to a single cluster, the fuzzy clustering philosophy, where each entity is allowed to be shared among more than one clusters and the possibilistic clustering philosophy, where what matters is the “degree of compatibility” of an entity with a given cluster.
Arguably, many classifiers have been devised in -, where most of them are mature in the offline environment, which does not necessitate swift model updates. Nevertheless, these classifier encompass a computationally prohibitive training phase, as the iterative learning scenario or the multi-pass learning step ought to be committed, where a retraining step benefiting from an up-to-date dataset, whenever a new knowledge is observed, should be enforced. Apart from a considerable computational cost, these classifiers impose the so-called catastrophic forgetting of previously valid * Corresponding Author.
G. Anavatti, and E. Lughofer Rule Pruning Procedure pClass is mounted by two rule pruning cursor, discovering the superfluous or outdated fuzzy rules. The first method is on a par with GENEFIS , , which emanates from the Extended Rule Significance (ERS) method. The subject of investigation of this method is inactive fuzzy rules, owning marginal leverages to the overall system output. More specifically, the ERS method can be mathematically expressed as follow: βi = m 2u +1 o =1 j =1 y ij o Vi u V (20) u P i i =1 where V i stands for the volume of i-th rule obtained by equation (8), thus representing the contribution of input part of the i-th rule, whereas y ip constitutes a K hyperplane of i-th fuzzy rule and in the case of MIMO architecture y ip = y k ip k =1 pointing out the total contribution of output part of i-th fuzzy rule.
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