搜索资源列表
SVM
- 回归分析，该支持向量机算法可用于预测，电力负荷、风力发电预测等(Regression analysis, the support vector machine algorithm can be used to forecast the power load, wind power prediction)
LS-SVM
- lssvm用于状态分类和预测，主要应用于信号处理以及智能算法等领域(LSSVM is used for state classification and prediction)
SVM神经网络的数据分类预测
- SVM神经网络的数据分类预测-葡萄酒种类识别(SVM for predicting the kind of wine)
SVM神经网络的回归预测分析---上证开盘指数预测
- SVM对数据训练，对数据处理预测分析，令可以作为分类的一种重要方法。(SVM to data training, to data processing forecast analysis.Order is an important method of classification.)
pso-svm one output
- 实现多输入单输出的预测功能，预测精度高，程序操作简单，易于修改应用。(The prediction function of multi input and single output is realized. The prediction accuracy is high, the program operation is simple, and it is easy to modify the application.)
SVM神经网络的数据分类预测-葡萄酒种类识别
- SVM神经网络的数据分类预测-葡萄酒种类识别(SVM neural network data classification prediction wine species identification)
SVM
- SVM处理图像 数据与处理 特征提取 训练模型 模型预测 得到结果(SVM processing image data and processing feature extraction training model model to predict the results)
svm
- 风电功率预测，通过改进的支持向量机模式进行预测，(wind power prediction)
svm参数优化
- 采用svm来做分类，一般能得到较满意的结果，但用svm做分类预测时需要调节相关的参数才能得到比较理想的预测分类准确率，那么svm的参数该如何选取？该程序主要说明如何更好地提升分类器性能。(Use svm to do the classification, the general can get more satisfactory results, but when using svm to do classification prediction need to adjust the relev
prediction SVM
- 用SVM做预测的matlab程序,内部程序详细，包含数据(a program to predict time series)
基于支持向量机的短期电力负荷预测
- 基于支持向量机的短期电力负荷预测，支持向量机，预测(Support vector machine based short-term power load forecasting, support vector machine (SVM), prediction)
基于PCA_FOA_SVR的股票价格预测研究_王卫红
- SVM应用举例,利用svm回归分析，预测股市指数，支持向量机回归(Application examples of svm)
SVM的回归预测分析——上证指数开盘预测
- 股票线性回归预测分析，利用svm线性回归算法分析预测，适合svm初学者(Stock Linear Regression Forecast Analysis)
PSO-SVM
- 将改进的粒子群算法和GA与SVM相结合，通过参数寻优构建新模型完成对空气质量指数的预测(The improved particle swarm optimization and genetic algorithm are combined with SVM. The prediction of air quality index (AQI) is completed by constructing a new model by parameter optimization.)
pso-svm电力负荷预测
- 电力负荷预测是电力系统规划的重要组成部分，也是电力系统经济运行的基础，其对电力系统规划和运行都极其重要。利用粒子群算法优化支持向量机更加高效准确预测电力负荷。(The power load forecasting is an important part of the power system planning, and it is also the basis of the economic operation of the power system. It is very important
GBDT+SVM
- 使用机器学习中的SVM，GBDT算法构建分类模型，做分类预测。并且对测试结果评估，模型保存。(Use SVM and GBDT algorithm in machine learning to build classification model and do classification prediction. And evaluate the test results and save the model.)
PSO-SVM
- 基于支持向量机负荷功率预测，使用粒子群算法进行参数寻优，供参考(Load Forecasting Based on Support Vector Machine)
SVM_tensorflow-master
- SVM通过tensorflow训练iris数据集，寻找最优参数，使误差最小化(SVM trains iris data set through tensorflow to find the optimal parameters and minimize the error.)
负荷预测算法包
- 负荷包预测算法，里面有SVM的详细说明，欢迎大家一起讨论(Load packet prediction algorithm, there is a detailed descr iption of SVM, welcome everyone to discuss)
SVMcgForRegress
- 支持向量机中的支持向量回归函数对数据进行预测(Support Vector Regression Function in Support Vector Machine to Predict Data)