典型文献
Quantum algorithm for soft margin support vector machine with hinge loss function
文献摘要：
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 on.However,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 set.To achieve it,an efficient quantum algorithm is proposed.Specifically,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 operator.Compared with its classical counterpart,this algorithm has a quadratic speedup on the number of training data points.It is worth emphasizing that the optimal model parameters obtained by this algorithm are in the classical form rather than in the quantum state form.This enables the algorithm to classify new data at little cost when the optimal model parameters are determined.
文献关键词：
中图分类号：
作者姓名：
Liu Hailing;Zhang Jie;Qin Sujuan;Gao Fei
作者机构：
State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China;State Key Laboratory of Cryptology,P.O.Box 5159,Beijing 100878,China
文献出处：
引用格式：
[1]Liu Hailing;Zhang Jie;Qin Sujuan;Gao Fei-.Quantum algorithm for soft margin support vector machine with hinge loss function)[J].中国邮电高校学报（英文版）,2022(04):32-41
A类：
B类：
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AB值：
0.542552
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