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Evolvable hardware has shown to be a promising approach for prosthetic hand controllers as it features self adaptation, fast training, and a compact system-on-chip implementation. Besides these intriguing features, the classification performance is paramount to success for any classifier. However, evolvable hardware classifiers have not yet been sufficiently compared to state-of-the-art conventional classifiers. In this paper, the authors compare two evolvable hardware approaches for signal classification to three conventional classification techniques: k-nearest-neighbor, decision trees, and support vector machines.
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