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[Mathimatics-Numerical algorithms] ihlf-svr-rfn DL : 0: 这是一个师兄编的程序，用于多类分类和函数回归。
Date : 2008-10-13 Size : 138.61kb User : 王一笑
[Mathimatics-Numerical algorithms] ihlf-svr-rfn DL : 0: 这是一个师兄编的程序，用于多类分类和函数回归。-This is a senior allocation procedures, for use in multi-category classification and regression function.
Date : 2025-12-25 Size : 138kb User : 王一笑
[Mathimatics-Numerical algorithms] ssvr-crs DL : 0: 本人编的一个程序，用来求解支持矢量，可以用于分类和回归。-I made a procedure to solve the support vector, can be used for classification and regression.
Date : 2025-12-25 Size : 1.6mb User : 王一笑
[Mathimatics-Numerical algorithms] lpsvr DL : 0: 基于线性规划的回归支持向量机源程序，开发环境Visual C++6.0，控制台程序-Based on linear programming support vector machine regression source code, development environment, Visual C++ 6.0, Console Application
Date : 2025-12-25 Size : 69kb User : 谢宏
[Mathimatics-Numerical algorithms] svm DL : 0: 基于遗传算法的支持向量回归机参数选取，针对支持向量回归机( support vector regression , SVR) 的参数选择问题,提出了基于遗传算法的 SVR 参数自动确定方法。分-Based on genetic algorithm parameter selection of Support Vector Regression
Date : 2025-12-25 Size : 230kb User : 王明
[Mathimatics-Numerical algorithms] svr DL : 0: 79419096svr一维支持向量机回归以及二维支持向量机回归-79419096svr one-dimensional support vector regression and two-dimensional support vector regression
Date : 2025-12-25 Size : 230kb User : myzone
[Mathimatics-Numerical algorithms] SMO-code DL : 0: smo算法是与svr（支持向量机回归）和svc（支持向量机分类）具有相似数学形式，并在此基础上提出的一种用于SVR的简化算法。-smo algorithm is svr (support vector machine regression) and svc (SVM) with similar mathematical form, and puts forward a simplified algorithm for SVR.
Date : 2025-12-25 Size : 1kb User : heyufeng
[Mathimatics-Numerical algorithms] svm4 DL : 0: 　-s svm类型：SVM设置类型(默认0) 　　0 -- C-SVC 　　1 --v-SVC 　　2 – 一类SVM 　　3 -- e -SVR 　　4 -- v-SVR 　　-t 核函数类型：核函数设置类型(默认2) 　　0 – 线性：u v 　　1 – 多项式：(r*u v + coef0)^degree 　　2 – RBF函数：exp(-r|u-v|^2) 　　3 –sigmoid：tanh(r*u v + coef0) 　　-d degree：核函数中的degree设置(针对多项式核函数)(默认3) 　　-g r(gama)：核函数中的gamma函数设置(针对多项式/rbf/sigmoid核函数)(默认1/ k) 　　-r coef0：核函数中的coef0设置(针对多项式/sigmoid核函数)((默认0) 　　-c cost：设置C-SVC，e -SVR和v-SVR的参数(损失函数)(默认1) 　　-n nu：设置v-SVC，一类SVM和v- SVR的参数(默认0.5) 　　-p p：设置e -SVR 中损失函数p的值(默认0.1) 　　-m cachesize：设置cache内存大小，以MB为单位(默认40) 　　-e eps：设置允许的终止判据(默认0.001) 　　-h shrinking：是否使用启发式，0或1(默认1) 　　-wi weight：设置第几类的参数C为weight*C(C-SVC中的C)(默认1) 　　-v n: n-fold交互检验模式，n为fold的个数，必须大于等于2--s svm_type : set type of SVM (default 0) 0-- C-SVC 1-- nu-SVC 2-- one-class SVM 3-- epsilon-SVR 4-- nu-SVR -t kernel_type : set type of kernel function (default 2) 0-- linear: u *v 1-- polynomial: (gamma*u *v+ coef0)^degree 2-- radial basis function: exp(-gamma*|u-v|^2) 3-- sigmoid: tanh(gamma*u *v+ coef0) 4-- precomputed kernel (kernel values in training_instance_matrix) -d degree : set degree in kernel function (default 3) -g gamma : set gamma in kernel function (default 1/k) -r coef0 : set coef0 in kernel function (default 0) -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1) -m cachesize : set cache memory size in MB (default 100) -e epsilon : set tolerance of termination criterion (default 0.001) -h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1) -b
Date : 2025-12-25 Size : 17kb User : little863
[Mathimatics-Numerical algorithms] newsvr DL : 0: 新的支持向量机回归算法 An Accurate Online Support Vector Regression (AOSVR) algorithm is introduced, which efficiently updates a trained SVR function whenever a sample is added to or removed from the training set. The updated SVR function is identical to the one that would be produced by a batch algorithm. Applications of AOSVR both in an online and in a crossvalidation scenario are presented-Accurate Online Support Vector Regression An Accurate Online Support Vector Regression (AOSVR) algorithm is introduced, which efficiently updates a trained SVR function whenever a sample is added to or removed from the training set. The updated SVR function is identical to the one that would be produced by a batch algorithm. Applications of AOSVR both in an online and in a crossvalidation scenario are presented
Date : 2025-12-25 Size : 251kb User : xing yongzhong
[Mathimatics-Numerical algorithms] The11 DL : 0: 基于SVR的期权价格预测模型The option price based on SVR prediction model-The option price based on SVR prediction model
Date : 2025-12-25 Size : 637kb User :
[Mathimatics-Numerical algorithms] test_ctly DL : 0: 简单的支持向量机回归应用，应用于球磨机实验中。(Simple support vector machine regression application)
Date : 2025-12-25 Size : 1kb User : 小克·
[Mathimatics-Numerical algorithms] online-svr DL : 0: 实现在线的SVR算法，python版本。(online svr, python version.)
Date : 2025-12-25 Size : 1.35mb User : brbaaa
[Mathimatics-Numerical algorithms] Dissertation-ARIMA_SVR-prediction-master DL : 0: 基于时间序列分析ARIMA和SVR组合模型的预测(Prediction of ARIMA and SVR combined models based on time series analysis)
Date : 2025-12-25 Size : 131kb User : yongqiang123