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MATLAB与机器学习

于 2020-07-08 发布
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代码说明：

说明：  此为机器学习中的常见算法的matlab代码，代码注释详细，易上手(This is a common algorithm in machine learning matlab code, code comments in detail, easy to use)

文件列表：

MATLAB与机器学习, 0 , 2019-01-22
MATLAB与机器学习\BP神经网络, 0 , 2019-01-22
MATLAB与机器学习\BP神经网络\html, 0 , 2019-01-22
MATLAB与机器学习\BP神经网络\html\main.html, 8514 , 2015-08-03
MATLAB与机器学习\BP神经网络\html\main.png, 2879 , 2015-08-03
MATLAB与机器学习\BP神经网络\html\main_01.png, 6883 , 2015-08-03
MATLAB与机器学习\BP神经网络\main.m, 1516 , 2018-11-16
MATLAB与机器学习\BP神经网络\spectra_data.mat, 171497 , 2010-10-14
MATLAB与机器学习\RBF、GRNN和PNN神经网络, 0 , 2019-01-22
MATLAB与机器学习\RBF、GRNN和PNN神经网络\html, 0 , 2019-01-22
MATLAB与机器学习\RBF、GRNN和PNN神经网络\html\main_GRNN_PNN.html, 17037 , 2015-08-15
MATLAB与机器学习\RBF、GRNN和PNN神经网络\html\main_GRNN_PNN.png, 3184 , 2015-08-15
MATLAB与机器学习\RBF、GRNN和PNN神经网络\html\main_GRNN_PNN_01.png, 8204 , 2015-08-15
MATLAB与机器学习\RBF、GRNN和PNN神经网络\html\main_GRNN_PNN_02.png, 9510 , 2015-08-15
MATLAB与机器学习\RBF、GRNN和PNN神经网络\html\main_GRNN_PNN_03.png, 7940 , 2015-08-15
MATLAB与机器学习\RBF、GRNN和PNN神经网络\html\main_RBF.html, 8024 , 2015-08-15
MATLAB与机器学习\RBF、GRNN和PNN神经网络\html\main_RBF.png, 3103 , 2015-08-15
MATLAB与机器学习\RBF、GRNN和PNN神经网络\html\main_RBF_01.png, 7295 , 2015-08-15
MATLAB与机器学习\RBF、GRNN和PNN神经网络\iris_data.mat, 1105 , 2010-10-17
MATLAB与机器学习\RBF、GRNN和PNN神经网络\main_GRNN_PNN.m, 2780 , 2015-08-15
MATLAB与机器学习\RBF、GRNN和PNN神经网络\main_RBF.m, 1026 , 2015-09-13
MATLAB与机器学习\RBF、GRNN和PNN神经网络\spectra_data.mat, 171497 , 2010-10-14
MATLAB与机器学习\决策树与随机森林, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\DecisionTrees, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\DecisionTrees\data.mat, 86267 , 2009-11-29
MATLAB与机器学习\决策树与随机森林\Class_8_Code\DecisionTrees\html, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\DecisionTrees\html\main.html, 16031 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\DecisionTrees\html\main.png, 3675 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\DecisionTrees\html\main_01.png, 39119 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\DecisionTrees\html\main_02.png, 5341 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\DecisionTrees\html\main_03.png, 29181 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\DecisionTrees\html\main_04.png, 33141 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\DecisionTrees\main.m, 2721 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\data.mat, 86267 , 2009-11-29
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\html, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\html\main.html, 16143 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\html\main.png, 2407 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\html\main_01.png, 8813 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\html\main_02.png, 5388 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\main.m, 2591 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\classRF_predict.m, 2166 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\classRF_train.m, 14829 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\Compile_Check, 856 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\compile_linux.m, 557 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\compile_windows.m, 1589 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\data, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\data\twonorm.mat, 48856 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\data\X_twonorm.txt, 96300 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\data\Y_twonorm.txt, 600 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\Makefile, 2693 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\Makefile.windows, 2523 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\mexClassRF_predict.mexw64, 26624 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\mexClassRF_train.mexw64, 43520 , 2015-10-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\precompiled_rfsub, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\precompiled_rfsub\linux64, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\precompiled_rfsub\win32, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\precompiled_rfsub\win32\rfsub.o, 6848 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\precompiled_rfsub\win64, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\precompiled_rfsub\win64\rfsub.o, 9840 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\README.txt, 3128 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\rfsub.o, 9840 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\src, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\src\classRF.cpp, 33889 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\src\classTree.cpp, 8947 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\src\cokus.cpp, 7678 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\src\cokus_test.cpp, 1189 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\src\mex_ClassificationRF_predict.cpp, 5225 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\src\mex_ClassificationRF_train.cpp, 8545 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\src\qsort.c, 4676 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\src\rf.h, 5186 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\src\rfsub.f, 15851 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\src\rfutils.cpp, 9609 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\src\twonorm_C_wrapper.cpp, 9865 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\tempbuild, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\test_ClassRF_extensively.m, 604 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\tutorial_ClassRF.m, 10403 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\twonorm_C_devcpp.dev, 1783 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Class_C\Version_History.txt, 1311 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\Compile_Check_kcachegrind, 611 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\Compile_Check_memcheck, 623 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\compile_linux.m, 952 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\compile_windows.m, 801 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\data, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\data\diabetes.mat, 265664 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\data\X_diabetes.txt, 110942 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\data\Y_diabetes.txt, 11492 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\diabetes_C_devc.dev, 1293 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\Makefile, 1774 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\README.txt, 2623 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\regRF_predict.m, 986 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\regRF_train.m, 12863 , 2009-05-17
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\src, 0 , 2019-01-22
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\src\cokus.cpp, 7678 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\src\cokus_test.cpp, 1189 , 2009-04-25
MATLAB与机器学习\决策树与随机森林\Class_8_Code\RandomForest\randomforest-matlab\RF_Reg_C\src\diabetes_C_wrapper.cpp, 11673 , 2009-05-17

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  • dvhop
    主要介绍了dvhop算法的源代码，先计算平均每跳的距离，再根据跳数求出锚节点与未知节点的距离，再用三边测量法求出未知节点坐标(Dvhop algorithm introduces the source code, to calculate the average distance of each jump, then jump a few calculated in accordance with anchor nodes and unknown distance from the node, and then calculated trilateration unknown node coordinates)
    2009-05-21 19:16:11下载
    积分：1
  • Kmeans
    Kmeans algorithm is the most popular algorithm for clustering
    2011-02-14 16:53:06下载
    积分：1
  • adaboost1
    adaboost代码，比较简单。但方便了解Adaboost算法的基本原理(The aim of the project is to provide a source of the meta-learning algorithm known as AdaBoost to improve the performance of the user-defined classifiers. )
    2013-08-24 11:12:16下载
    积分：1
  • S_DBWebApplication1
    S_DBWEbapplication 实例 作为例子参考 讲解演示器作用(S_DBWEbapplication instance as an example to explain the reference to the role of presenter)
    2013-11-25 22:25:51下载
    积分：1
  • GDOP_6_A
    多星座导航系统GDOP递推特性的仿真程序(GDOP recursive multi-constellation navigation system features simulation program)
    2015-03-13 10:35:29下载
    积分：1
  • BFGS
    拟牛顿法和最速下降法(Steepest Descent Methods)一样只要求每一步迭代时知道目标函数的梯度。通过测量梯度的变化，构造一个目标函数的模型使之足以产生超线性收敛性。这类方法大大优于最速下降法，尤其对于困难的问题。另外，因为拟牛顿法不需要二阶导数的信息，所以有时比牛顿法(Newton s Method)更为有效。如今，优化软件中包含了大量的拟牛顿算法用来解决无约束，约束，和大规模的优化问题。(The quasi-Newton method and the Steepest Descent Methods only require that each step iterations know the gradient of the objective function. By measuring the change of the gradient, constructing a model of the objective function is sufficient to produce superlinear convergence. This method is much better than the steepest descent method, especially for difficult problems. In addition, because the quasi-Newton method does not require information on the second derivative, it is sometimes more effective than the Newton s Method. Today, the optimization software contains a large number of quasi-Newton algorithm used to solve the unconstrained, constraint, and large-scale optimization problems.)
    2017-05-05 10:28:29下载
    积分：1
  • BOC_BPSK
    GPS信号的BPSK调制以及BOC信号的功率谱的ｍａｔｌａｂ仿真程序，可以将两者结合起来比较学习(GPS signals BPSK and BOC modulation signal of the power spectrum matlab simulation program, you can compare the two to combine learning)
    2020-10-28 14:29:58下载
    积分：1
  • cdma
    采用直扩方式构造的CDMA系统，最后给出CDMA系统的误码率曲线(Constructed using DS-CDMA systems approach, and finally gives CDMA system bit error rate curve)
    2009-11-06 00:00:01下载
    积分：1
  • dizhengbo
    用c语言进行地震波模拟，地震波被看做弹性波，在不均匀介质中传播的信息。(Seismic wave simulation using c language, seismic waves are seen as an elastic wave propagation in inhomogeneous information medium.)
    2014-02-11 17:14:06下载
    积分：1
  • 弥散对比
    数值模拟波浪力，根据莫里森方程，求单一深度波浪力数值模拟的matlab语言程序(Numerical simulation of wave forces)
    2020-06-25 05:40:02下载
    积分：1
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