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Study on the problems of support vector machines parameters optimization． The prediction precision of
Support vector machines model and generalization ability depend on its parameters reasonable choice． The problems of
time － consuming and easy falling into the local optimal value exist in traditional support vector machine parameters
optimization algorithm，and support vector machine prediction precision is low． In order to solve the problems，the
paper puts forward a method based on depth first search of SVM parameters optimization method ( DFS － SVM) ． DFS
－ SVM takes SVM parameters optimization as a combinatorial optimization problem and the RMSE as optimization
goal，uses depth first search to select SVM parameters，and tests DFS － SVM performaces through three standard data
set． Simulation experiment results show that the DFS － SVM prediction accuracy is improved and the training time is
shorten greatly． It provides a new effective solution for SVM parameters optimizat