搜索资源列表
psoSVM
- 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
PS0-SVR
- ：针对发酵过程中生物参数难以实时在线测量的问题，建立了用于生物参数状态预估的 支持向量机软测量模型。考虑到该支持向量回归(SVR)模型的复杂性和冷化特征取决于其三 个参数 ，c， 能否取到最优值，采用粒子群优化(PSO)算法实现对参数 ，c， 的同时寻优。在 此基础上，以饲料用 ．甘露聚糖酶为对象，建立了基于PSO—SVR的发酵过程产物浓度状态预估 模型。发酵罐控制结果表明：该模型具有很好的学习精度和泛化能力，可实现对 ．甘露聚糖酶 产物浓度的实时在线预估。-In
FeatureSelection_MachineLearning
- 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
psopetsvr
- 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.
2016.11.03DE_SVR（差分进化）
- 以优化SVR算法的参数c和g为例，对DE（差分进化）算法MATLAB源码进行了详细中文注解。(Differential Evolution algorithm (DE) is a heuristic random search algorithm based on group differences. This algorithm is proposed by R.S and k.p. rice for solving Chebyshev polynomials. DE algorithm is
PSO-Based-SVR-master
- 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)
pso-svr代码
- 基于粒子群优化的向量回归预测分析 matlab代码(Support vector regression code with pso)
svr_pso - Libsvm
- SVR optimizaton using PSO with LibSVM library
PSO SVM粒子算法优化的支持向量机
- 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)