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
pso-svm
- 这是一个用pso优化SVM中的惩罚参数C和核参数g的MATLAB源码，简单易学-This is an optimization of SVM with the pso in the penalty parameter C and kernel parameter g of the MATLAB source code, easy to learn
PSwarmM_v2_1
- pso Developed by: Mahamed G.H. Omran (omran.m@gust.edu.kw) and Maurice Clerc
simple-PSO
- 简单的粒子群算法用以优化SVM的惩罚参数C和核参数g-Particle Swarm Optimization SVM parameters C and g
psoLSSVMcgForClass
- 利用PSO优化SVM中的c和g c1:初始为1.5,pso参数局部搜索能力 c2:初始为1.7,pso参数全局搜索能力 maxgen:初始为200,最大进化数量 sizepop:初始为20,种群最大数量-Support Vector Machine Optimized by Particle Swarm Optimization
PSO
- 利用改进的粒子群算法解决旅行商问题，即g改进的PSO解决TSP,在调用程序时，直接输入pso（c,n），c为距离矩阵，请自己给出，n为粒子群个数-Improved particle swarm algorithm to solve the traveling salesman problem, ie g Improved PSO to solve the TSP
xox_Wave_NN
- The Wavelet Neural Network This is simple example for using of modified Morlet neural network. Levenberg-Marquardt with numerical Jacobian calculation implemented. Easy to use with other optimization algorithem e.g GA,PSO, etc. The funct
Performance-comparison-of-G-A--Pso-and-He-in-beam
- Performance comparison of Genetic Algorithm, Particle swarm optimization and Hybrid evolutionary algorithm in beamforming
PSO_SVM
- SVM用于分类时的参数优化，粒子群优化算法，用于优化核函数的c,g两个参数-SVM PSO
1236874SVMcgForRegress
- 粒子群优化支持向量机算法 实现对c g 的改进-PSO support vector machine algorithm to achieve the c g Improvement
chapter15_PSO
- svm 的参数优化，利用pso(粒子群优化算法)选择最优参数c g，最终提高训练集的分类准确率，更好的提高分类器性能-Svm parameter optimization, the use of pso (particle swarm optimization algorithm) to the optimal parameter c g, and ultimately improve the training set classification accuracy, better impr
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
psoSVM
- 采用粒子群算法优化支持向量机的两个参数c与g，以进行后续的实验(Particle swarm optimization (PSO) is used to optimize two parameters c and G for support vector machines for subsequent experiments)
PSO-SVM
- 用粒子群算法优化SVM中惩罚系数C和高斯核函数g的参数(Using particle swarm optimization to optimize parameters of penalty coefficient C and Gauss kernel function g in SVM)
pso-SVM
- 利用粒子群法寻找出c,g两个参数，从而达到优化支持向量机的目的