如hinge lossf(w)=max(1−y⟨w,x⟩,0)及神经网络中使用的ReLU激活函数不可微。 第二个区别是,SD不是一种下降方法,也就是说,可能出现f(xt+1)>f(xt)。不管使用什么步长,目标函数都可以保持不变或甚至在迭代中增加。事实上,一个常见的误解是,在SD方法中,次梯度告诉我们应该往哪个方向去降低函数值。
This approach was developed by replacing the hinge loss function in the conventional support vector machine (SVM) with a generalized pinball loss function. We show that SG-GPSVM is convergent and that it approximates the conventional generalized pinball support vector machine (GPSVM). Further, the...