Hierarchical SVM structure and support vector memory optimization achieve up to 56% memory savings in activity recognition experiments, with a controllable accuracy trade-off.
Real-time classification of electromyographic signals for robotic control,
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Resource-Efficient Computing in Wearable Systems
Hierarchical SVM structure and support vector memory optimization achieve up to 56% memory savings in activity recognition experiments, with a controllable accuracy trade-off.