Adaptive and Intelligent Systems: Third International by Abdelhamid Bouchachia (eds.) PDF

By Abdelhamid Bouchachia (eds.)

This publication constitutes the lawsuits of the overseas convention on Adaptive and clever structures, ICAIS 2014, held in Bournemouth, united kingdom, in September 2014. the nineteen complete papers integrated in those court cases including the abstracts of four invited talks, have been conscientiously reviewed and chosen from 32 submissions. The contributions are geared up lower than the subsequent topical sections: advances in function choice; clustering and category; adaptive optimization; advances in time sequence analysis.

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Extra info for Adaptive and Intelligent Systems: Third International Conference, ICAIS 2014, Bournemouth, UK, September 8-10, 2014. Proceedings

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J xij /N, . . , xDj ) be jth instance modified at ith position. For each data instance j, partial sensitivity is defined by Sij = (f (xij ) − yi )2 − (f (xj ) − yi ))2 , (1) where f is the neural network function and yi is the target value for ith data instance. Further, the sensitivity of the network to variable i is defined as: Si = j Sij N (2) In our implementation of SBP, the algorithm starts with the full set of features (D = 20). At each step, a target neural network is trained. Further, its sensitivity is computed for particular inputs and the feature, for which the sensitivity is smallest is removed from the data.

The SUN dataset captures a full variety of 899 scene categories and is by far the largest scene recognition dataset. We have used 397 well-sampled categories for which there are at least 100 unique photographs. Following [14], we have used 50 images per class for training. Fig. 1 shows some sample images from different scenes in the dataset. In order to evaluate the performance of our approach for Codebook generation, we have used four well-known metrics, overall accuracy, average precision, average recall, and F-measure [15].

IEEE Transactions on Pattern Analysis and Machine Intelligence 17(10), 1010–1014 (1995) 15. : Unsupervised texture classification: Automatically discover and classify texture patterns. Image and Vision Computing 26(5), 647–656 (2008) 16. : An introduction to hidden Markov models. IEEE ASSP Magazine 3(1), 4–16 (1986) 17. : Texture classification by means of hmm modeling of am-fm features. In: Proc. ICIP, pp. 182–185 (2001) 18. : The truth of the f-measure. School of Computer Science. University of Manchester (2007) 19.

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