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As one of the most recognized models in machine learning, the conditional random fields (CRF) has been widely used in many applications. As the parameter estimation of CRF is highly time-consuming, how to improve the performance of CRF has received significant attention, in particular in the big data environment. To deal with large-scale data, CPU-based or(More)
Iterative SpMV (ISpMV) is a key operation in many graph-based data mining algorithms and machine learning algorithms. Along with the development of big data, the matrices can be so large, perhaps billion-scale, that the SpMV can not be implemented in a single computer. Therefore, it is a challenging issue to implement and optimize SpMV for large-scale data(More)
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