Soft margin support vector machine(SVM)with hinge loss function is an important classification algorithm,which has been widely used in image recognition,text classification and so ***,solving soft margin SVM with hing...
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Soft margin support vector machine(SVM)with hinge loss function is an important classification algorithm,which has been widely used in image recognition,text classification and so ***,solving soft margin SVM with hinge loss function generally entails the sub-gradient projection algorithm,which is very time-consuming when processing big training data *** achieve it,an efficient quantum algorithm is ***,this algorithm implements the key task of the sub-gradient projection algorithm to obtain the classical sub-gradients in each iteration,which is mainly based on quantum amplitude estimation and amplification algorithm and the controlled rotation *** with its classical counterpart,this algorithm has a quadratic speedup on the number of training data *** is worth emphasizing that the optimal model parameters obtained by this algorithm are in the classical form rather than in the quantum state *** enables the algorithm to classify new data at little cost when the optimal model parameters are determined.
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