Xiaogao Yu

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K-nearest neighbor (KNNC) classifier is the most popular non-parametric classifier. But it requires much classification time to search k nearest neighbors of an unlabelled object point, which badly affects its efficiency and performance. In this paper, an adaptive k-nearest neighbors classifier (AKNNC) is proposed. The algorithm can find k nearest neighbors(More)
Given n data points in d-dimensional space, k nearest neighbors searching involves determining k nearest of these data points to a given query point. A depth-first adaptive kNN searching (DAKNNs) algorithm is proposed. The algorithm finds k nearest neighbors of the query point in a small hyperball in order to improve the efficiencies. It firstly determines(More)
A distributed resource based collaborative workflow management system (BKMS) was proposed in the paper, which will greatly ensure the efficient of cooperation among partner enterprises. In the paper, enterprises and their partners construct a manufacturing grid framework, all the resources geographically distributed in different enterprise are all looked as(More)
K-Nearest Neighbors search (KNNS) in high-dimensional feature spaces is an important paradigm in pattern recognition. Existing centralized KNNS does not scale up to large volume of data because the response time is linearly increasing with the size of the searched file. In this article, an adaptive distributed A-nearest neighbor search algorithm (P2PAKNNS)(More)
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