[{"title":"( 28 个子文件 9.2MB ) 一些关于支持向量机的文献","children":[{"title":"SVM","children":[{"title":"基于小波消噪的混沌多元回归日径流预测模型.pdf <span style='color:#111;'> 645.47KB </span>","children":null,"spread":false},{"title":"多元平稳时间序列ARIMAX模型的应用.pdf <span style='color:#111;'> 221.09KB </span>","children":null,"spread":false},{"title":"小波支持向量机.pdf <span style='color:#111;'> 446.93KB </span>","children":null,"spread":false},{"title":"基于支持向量机的异常检测.pdf <span style='color:#111;'> 318.89KB </span>","children":null,"spread":false},{"title":"神经网络应用于多元变量时间序列的建模研究.pdf <span style='color:#111;'> 370.98KB </span>","children":null,"spread":false},{"title":"基于尺度核函数的最小二乘支持向量机.pdf <span style='color:#111;'> 294.63KB </span>","children":null,"spread":false},{"title":"一种基于Morlet小波核的约简支持向量机.pdf <span style='color:#111;'> 285.58KB </span>","children":null,"spread":false},{"title":"基于多重核学习支持向量回归的混沌时间序列预测.pdf <span style='color:#111;'> 489.92KB </span>","children":null,"spread":false},{"title":"基于SVM的混沌时间序列分析.pdf <span style='color:#111;'> 276.50KB </span>","children":null,"spread":false},{"title":"一种混合核函数支持向量机算法.pdf <span style='color:#111;'> 277.69KB </span>","children":null,"spread":false},{"title":"核密度估计在预测风险价值中的应用.pdf <span style='color:#111;'> 439.57KB </span>","children":null,"spread":false},{"title":"基于一类局部固定核的支持向量回归建模.pdf <span style='color:#111;'> 331.67KB </span>","children":null,"spread":false},{"title":"支持向量机中核函数的性能评价策略.pdf <span style='color:#111;'> 247.68KB </span>","children":null,"spread":false},{"title":"基于多元局部多项式方法的混沌时间序列预测.pdf <span style='color:#111;'> 498.40KB </span>","children":null,"spread":false},{"title":"高斯小波支持向量机的研究.pdf <span style='color:#111;'> 410.67KB </span>","children":null,"spread":false},{"title":"基于支持向量机方法对非平稳时间序列的预测.pdf <span style='color:#111;'> 495.71KB </span>","children":null,"spread":false},{"title":"支持向量机的正定核_英文_.pdf <span style='color:#111;'> 299.87KB </span>","children":null,"spread":false},{"title":"尺度核函数在最小二乘支持向量机信号逼近中的应用.pdf <span style='color:#111;'> 360.97KB </span>","children":null,"spread":false},{"title":"支持向量机核函数及优化研究.pdf <span style='color:#111;'> 558.00KB </span>","children":null,"spread":false},{"title":"新型小波支持向量机在波动率预测中的实证研究.pdf <span style='color:#111;'> 565.03KB </span>","children":null,"spread":false},{"title":"基于ARIMA的多元时间序列神经网络预测模型研究.pdf <span style='color:#111;'> 207.71KB </span>","children":null,"spread":false},{"title":"支持向量机核函数选择的研究.pdf <span style='color:#111;'> 342.75KB </span>","children":null,"spread":false},{"title":"股指期货信息内含股价变动信息的挖掘_小波框架与支持向量回归的金融建模应用.pdf <span style='color:#111;'> 245.91KB </span>","children":null,"spread":false},{"title":"基于EOFSVD模型的多元时间序列相关性研究及预测.pdf <span style='color:#111;'> 348.31KB </span>","children":null,"spread":false},{"title":"支持向量机最优参数选择的研究.pdf <span style='color:#111;'> 353.14KB </span>","children":null,"spread":false},{"title":"支持向量回归中核函数和超参数选择方法综述.pdf <span style='color:#111;'> 469.54KB </span>","children":null,"spread":false},{"title":"基于小波的支持向量机算法研究.pdf <span style='color:#111;'> 295.91KB </span>","children":null,"spread":false},{"title":"不同种类支持向量机算法的比较研究.pdf <span style='color:#111;'> 282.46KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]