Abstract: With the constant increasing of data scale,the parallel design of support vector machine(SVM) has become a hot research topic in data mining field.In view of the problems in model training including slow optimization and large memory,we proposed a new parallel SVM algorithm(SP-SVM) based on Spark.First of all,this paper implemented algorithm using Spark parallel computing framework.Secondly,this paper analyzed the performance of the parallel operator and optimized the algorithm in parallel design scheme,solving the problem of low efficiency that cascade training model encounters.Experimental results show that the new parallel training method can save more training time and greatly improve the efficiency in the case of a small precision loss.

Key words: Parallel computing,Support vector machine,Large scale data,Cascade model,Spark