利用微粒群算法（pso）优化支持向量机（SVM）的参数-c,-g-Using pso optimized support vector machine parameters-c,-g

Feature selection methods for machine learning algorithms such as SVR, including one filter-based method (CFS) and two wrapper-based methods (GA and PSO). The gridsearch is for the grid search for the optimal hyperparemeters of SVR. The SVM_CV is for

一个用于预测石油污染物降解率的SVR程序，模型参数采用PSO算法优化选择。供大家参考！-a SVR model for prediction of oil degradation rate. Model s parameters was optimized by PSO algorithm.

该文件为粒子群算法优化支持向量机模型(This document is optimized by Particle Swarm Optimization (SVM) model)

使用PSO优化SVR支持向量机模型的代码，有详细的输出及输出，以及代码说明(Use PSO to optimize SVR support vector machine model code, with detailed output and output, as well as code descr iption)