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

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 may possibly 2014. There are 34 general papers out of 60 submissions, additionally five submissions have been authorised as brief papers and 15 papers have been authorized for 4 exact periods. They take care of emergent issues of man-made intelligence and are available from the SETN major convention in addition to from the subsequent distinctive periods on motion languages: thought and perform; computational intelligence concepts for bio sign research and evaluate; online game man made intelligence; multimodal advice platforms and their purposes 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

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15–28, 2014. © Springer International Publishing Switzerland 2014 16 M. G. Anavatti, and E. Lughofer knowledge due to its fixed learning capacity, where the previously sound rules are omitted with a set of totally new rules. These classifiers are also less flexible or adaptive to cope with regime shifting and drifting properties of the system being solved as the number of fuzzy rules or nodes is pre-fixed. Moreover, the knowledge building process is not automated, so that the classifiers cannot reflect the degree of nonlinearity and deal with the possible non stationary of learning environments.

Sequential Sparse Adaptive Possibilistic Clustering 31 The paper is organized as follows. In section 2, the SAPCM is described, while in section 3, the proposed SeqSAPCM algorithm is presented in detail. Section 4 contains simulation results that allow the assessment of the performance of the proposed algorithm. Finally, section 5 concludes the paper. , N } be a set of N , l-dimensional data vectors that are to be clustered. , m} be a set of m vectors that will be used for the representation of the clusters formed in X.

The point of departure is the introduction of the within class scatter matrix and between class scatter matrix S b , S w as follows: 1 W − N  K tr ( S w ) = tr ( K ) −  W tr ( S b ) = Note that the symbol ΣW means the sum of matrix W in every dimension (28) (29) W . , K 2 K  K= .................................... , K KK  (31) where K represents a kernel-Gram-matrix. One may comprehend, that K 11 ∈ ℜ N1× N1 demonstrates a kernel-Gram-sub-matrix, emanating from data in class 1, whereas K 12 ∈ ℜ N1 × N 2 constitutes a kernel-Gram-sub-matrix, originating from data in class 1 and 2 and so on.

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