Adaptive and Intelligent Systems: Third International - download pdf or read online

By Abdelhamid Bouchachia (eds.)

ISBN-10: 331911297X

ISBN-13: 9783319112978

ISBN-10: 3319112988

ISBN-13: 9783319112985

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

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

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They usually use low level features such as color, texture, etc. One of the widely used methods in this category is the GIST representation which uses perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) in order to represent the dominant spatial structure of a scene [3]. On the other hand, local descriptors are computed at multiple points in the image in order to represent the characteristics of the different regions in the image; thus allowing a better representation of the image content.

In Table 2, one can find the order, in which the features were removed from the original set. Although we averaged 20 runs, the feature selection was the same for most runs (19 of 20), thus we show the most typical result. The air temperature set points in active zones number 8 and 9 (zones 10 and 11 in Fig. 2 were the worst features filtered out for both networks. The findings from this paper will be directly used in our recent experiments with optimization of building heating. However, in such a real case, the training data generated by the procedure described here are not sufficient for a proper optimization.

1 37 Evaluation We have tested our approach on the SUN dataset. 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].

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Adaptive and Intelligent Systems: Third International Conference, ICAIS 2014, Bournemouth, UK, September 8-10, 2014. Proceedings by Abdelhamid Bouchachia (eds.)


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