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Compared to single classifiers, ensemble learning offers significant and stable performance improvement, while ensemble pruning can improve efficiency and performance of the ensembles further, so both of them are hot topics, not only in traditional machine learning but also in recent data stream mining scopes. Aiming to provide a uniform platform and(More)
Due to an increasing need for flexibility, embedded systems embody more and more programmable processors as their core components. Because of silicon area and power considerations, the corresponding instruction sets are often highly encoded to minimize code size for given performance requirements. This has hampered the development of robust optimizing(More)
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