By Sourav S. Bhowmick, Curtis Dyreson, Christian S. Jensen, Mong Li Lee, Agus Muliantara, Bernhard Thalheim
These volumes set LNCS 8421 and LNCS 8422 constitutes the refereed complaints of the nineteenth foreign convention on Database structures for complex functions, DASFAA 2014, held in Bali, Indonesia, in April 2014. The sixty two revised complete papers awarded including 1 prolonged summary paper, four business papers, 6 demo displays, three tutorials and 1 panel paper have been rigorously reviewed and chosen from a complete of 257 submissions. The papers disguise the next issues: enormous information administration, indexing and question processing, graph facts administration, spatio-temporal information administration, database for rising undefined, info mining, probabilistic and unsure information administration, net and social info administration, defense, privateness and belief, key-phrase seek, info move administration and knowledge quality.
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Additional info for Database Systems for Advanced Applications: 19th International Conference, DASFAA 2014, Bali, Indonesia, April 21-24, 2014. Proceedings, Part II
1 Introduction Surveillance cameras have been widely used to monitor public places and help the police in criminal investigation to recognize and trace suspects in the crowd. The quality of the surveillance video seriously aﬀects the success rate of recognition. Unfortunately, most of time it is not easy to identify a person only according to the video content due to the low resolution rate of the image, insuﬃcient brightness or occlusion. Diﬀerent from the traditional research concentrated in pattern recognition of images, we take the time and location information of a surveillance video into account to identify a person according to his trajectory.
One instance of MTL is multi-output regression , which has been widely used in image tagging. However, existing methods have the following two main limitations. , dictionary size) is always very large in practice. In , a predeﬁned tag ontology is used to construct a graph structure hierarchy to reduce the dimensionality of the tag feature space. However, such prior knowledge on tag structure is often not available in real applications. Secondly, some tags are usually correlated with each other, which has not been well studied and exploited in existing image tagging methods.
This is obvious in data sets “Anneal”, “Audiology” and “Kr-vs-kp”. 04 0 20 40 60 Number of classifiers 80 100 (d) Wine Fig. 4. Comparison of prediction errors on data sets “Glass”, “Lymph”, “Mfeat-pixel” and “Wine” highest accuracy on pruning set. However our methods also show an outstanding result when the size of subensemble increases, at least is not worse than Eigcons method. The results in Figure 4 show that the performance by Eigcons and SubmEP G is almost the same on those data sets. From the ﬁgures, we can also see that our methods have the best performance when the pruning ratio is set between 8% and 20%.
Database Systems for Advanced Applications: 19th International Conference, DASFAA 2014, Bali, Indonesia, April 21-24, 2014. Proceedings, Part II by Sourav S. Bhowmick, Curtis Dyreson, Christian S. Jensen, Mong Li Lee, Agus Muliantara, Bernhard Thalheim
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