Improving SSA-SVM and it's application in face recognition
Support vector machine has been used for face recognition,in order to improve the accuracy of its classification recognition,this paper proposes an improved sparrow search algorithm to optimize the classification model of support vector machine parameters.Firstly,the use of Tent chaos mapping effectively improves the slow convergence speed caused by the lack of ergocity and poor diversity of the initial population.Secondly,the adaptive dynamic weight factor is introduced by drawing on the learning strategy of particle swarm algorithm and cloud model,balancing global and local search capabilities,and expanding the search scope of the algorithm.Finally,Levy flight is introduced to improve the algorithm's optimization ability and ability to jump out of local extremes.It is not difficult to see from the results of the benchmark function that ISSA converges faster,has more accurate search capabilities,and is easier to jump out of local extremes than the five algorithms of SSA,PSO,BOA and GA.The experimental results based on LFW face dataset show that the average recognition rate of the proposed method is 89.36％,which is better than other methods,and its effectiveness is verified.
NETL
NSTL
万方数据
维普
