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Multi-index hashing (MIH) is the state-of-the-art method for indexing binary codes, as it divides long codes into substrings and builds multiple hash tables. However, MIH is based on the dataset codes uniform distribution assumption, and will lose efficiency in dealing with non-uniformly distributed codes. Besides, there are lots of results sharing the same Hamming distance to a query, which makes...
Related data streams refer to data streams that can be joined together by matching their join attributes. Existing research on learning from related data streams is based on an assumption that all streams arrive at a central processing unit in a synchronous way, such that in an arbitrary sliding window, all tuples of the streams can be perfectly joined together. This assumption, however, does not...
Random forests ensemble classifier showed to be suitable for classifying multisource data such as lidar and RGB image for urban scene mapping. However, two major problems remain: (1) the class boundaries are not well classified, a common issue in classification (2) the data are highly imbalanced raising another issue more specific to urban scenes. In this paper, we propose a new ensemble method based...
Support Vector Machines (SVMs) are popular for pattern classification. However, training a SVM requires large memory and high processing time, especially for large datasets, which limits their applications. To speed up their training, we present a new efficient support vector selection method based on ensemble margin, a key concept in ensemble classifiers. This algorithm exploits a new version of...
Airborne lidar systems have become an alternative source for the acquisition of altimeter data. In addition to multi-echo laser scanner systems, full-waveform systems are able to record the whole backscattered signal for each emitted laser pulse. These data provide more information about the structure and the physical properties of the surface. This paper is focused on the classification of full-waveform...
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