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This paper proposes an efficient and flexible nearest-neighbor-based learning and recognition system on a programmable chip. The nearest neighbor search (NNS), which is a common computational problem in pattern recognition, is to find the minimal distance among all distances between an unknown sample and the complete set of the often high-dimensional reference database. In this paper, a reference...
Different from previous works that focus on the iterative clustering algorithm, a dual-stage hardware architecture that supports two kinds of moving averages for the on-line clustering algorithm is proposed. The architecture includes a set of memories that operates in ping-pong mode, so that distance computations and centroid updating can be processed in pipeline. The high-throughput parallel divider...
The reported hybrid algorithm with software-hardware cooperation for prototype-based learning uses dynamic K-means to intelligently produce new cluster centers as reference patterns. The single nearest neighbor matching (K-nearest neighbor (KNN), K=3D1) with full-hardware architecture is used for efficient recognition. Large numbers of training instances are dynamically partitioned into k clusters...
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