Kernel approximation is widely used to scale up kernel SVM training and prediction. However, the memory computation costs of models are still too large if we want deploy them on memory-limited devices such as mobile phones, smart watches IoT devices. To address this challenge, propose a novel computation-efficient model by using both binary embedding coefficients. First, an efficient way genera...