登录
首页 » matlab » classification_toolbox

classification_toolbox

于 2020-03-10 发布
0 294
下载积分: 1 下载次数: 2

代码说明:

说明:  多种基本分类训练,包括支持向量机,偏最小二乘,主成分分析和线性分析(A variety of basic classification training, including support vector machine, partial least squares, principal component analysis and linear analysis)

文件列表:

classification_toolbox_5.2\calc_class_param.m, 3488 , 2018-11-21
classification_toolbox_5.2\calc_class_string.m, 3035 , 2018-12-20
classification_toolbox_5.2\calc_qt_limits.m, 2094 , 2018-11-21
classification_toolbox_5.2\calc_reg_param.m, 1790 , 2018-11-21
classification_toolbox_5.2\cartcv.m, 6467 , 2018-12-04
classification_toolbox_5.2\cartfit.m, 3719 , 2018-12-04
classification_toolbox_5.2\cartpred.m, 2328 , 2018-12-04
classification_toolbox_5.2\class_gui.fig, 46054 , 2018-11-14
classification_toolbox_5.2\class_gui.m, 119168 , 2019-01-08
classification_toolbox_5.2\dacompsel.m, 4696 , 2018-12-04
classification_toolbox_5.2\dacv.m, 8252 , 2018-12-04
classification_toolbox_5.2\dafit.m, 6789 , 2018-12-04
classification_toolbox_5.2\damultinormality.m, 3392 , 2018-11-21
classification_toolbox_5.2\dapred.m, 3786 , 2018-12-04
classification_toolbox_5.2\data_pretreatment.m, 2903 , 2018-12-04
classification_toolbox_5.2\help\classparameters.htm, 8300 , 2018-12-04
classification_toolbox_5.2\help\download.htm, 2182 , 2018-11-11
classification_toolbox_5.2\help\example.htm, 13402 , 2018-12-04
classification_toolbox_5.2\help\example_plsda_01.gif, 11262 , 2018-11-26
classification_toolbox_5.2\help\example_plsda_02.gif, 5246 , 2016-01-29
classification_toolbox_5.2\help\example_plsda_03.gif, 10376 , 2018-11-26
classification_toolbox_5.2\help\example_plsda_04.gif, 15034 , 2018-11-26
classification_toolbox_5.2\help\example_plsda_05.gif, 20139 , 2018-11-26
classification_toolbox_5.2\help\example_plsda_06.gif, 24757 , 2018-11-26
classification_toolbox_5.2\help\example_plsda_07.gif, 26719 , 2018-11-26
classification_toolbox_5.2\help\example_plsda_08.gif, 9991 , 2018-11-26
classification_toolbox_5.2\help\example_plsda_09.gif, 10708 , 2018-11-26
classification_toolbox_5.2\help\example_plsda_10.gif, 8197 , 2016-01-29
classification_toolbox_5.2\help\footer.htm, 586 , 2018-11-11
classification_toolbox_5.2\help\gui.htm, 7698 , 2018-11-26
classification_toolbox_5.2\help\gui_1.gif, 8972 , 2018-11-21
classification_toolbox_5.2\help\gui_2.gif, 8013 , 2016-01-29
classification_toolbox_5.2\help\gui_3.gif, 19682 , 2018-11-21
classification_toolbox_5.2\help\gui_4.gif, 18254 , 2018-11-21
classification_toolbox_5.2\help\gui_5.gif, 37173 , 2016-01-29
classification_toolbox_5.2\help\gui_6.gif, 15792 , 2018-11-21
classification_toolbox_5.2\help\gui_7.gif, 48375 , 2018-11-22
classification_toolbox_5.2\help\gui_8.gif, 38618 , 2018-11-22
classification_toolbox_5.2\help\gui_9.gif, 25954 , 2018-11-22
classification_toolbox_5.2\help\gui_calculate.htm, 15964 , 2018-11-26
classification_toolbox_5.2\help\gui_file.htm, 3731 , 2018-11-21
classification_toolbox_5.2\help\gui_predict.htm, 4709 , 2018-11-21
classification_toolbox_5.2\help\gui_results.htm, 14486 , 2018-12-04
classification_toolbox_5.2\help\gui_view.htm, 6159 , 2018-11-21
classification_toolbox_5.2\help\header.htm, 1104 , 2018-11-21
classification_toolbox_5.2\help\index.htm, 4433 , 2018-11-21
classification_toolbox_5.2\help\license.htm, 3592 , 2018-11-21
classification_toolbox_5.2\help\logo_milano_chemometrics.jpg, 9422 , 2016-01-29
classification_toolbox_5.2\help\math_formula_accuracy.gif, 1195 , 2016-01-29
classification_toolbox_5.2\help\math_formula_confmat.gif, 3146 , 2016-01-29
classification_toolbox_5.2\help\math_formula_er.gif, 646 , 2016-01-29
classification_toolbox_5.2\help\math_formula_ner.gif, 1025 , 2016-01-29
classification_toolbox_5.2\help\math_formula_nk.gif, 616 , 2016-01-29
classification_toolbox_5.2\help\math_formula_precision.gif, 559 , 2016-01-29
classification_toolbox_5.2\help\math_formula_sensitivity.gif, 567 , 2016-01-29
classification_toolbox_5.2\help\math_formula_specificity.gif, 1171 , 2016-01-29
classification_toolbox_5.2\help\math_formula_wilks.gif, 554 , 2016-01-29
classification_toolbox_5.2\help\menu_lateral.htm, 2422 , 2018-11-21
classification_toolbox_5.2\help\references.htm, 5067 , 2018-11-21
classification_toolbox_5.2\help\releases.htm, 9284 , 2018-11-21
classification_toolbox_5.2\help\routines.htm, 7614 , 2018-12-04
classification_toolbox_5.2\help\style_structure.css, 671 , 2016-01-29
classification_toolbox_5.2\help\style_tables.css, 992 , 2016-01-29
classification_toolbox_5.2\help\style_text.css, 2919 , 2016-01-29
classification_toolbox_5.2\help\theory.htm, 21221 , 2018-12-04
classification_toolbox_5.2\help\web.htm, 3655 , 2018-11-11
classification_toolbox_5.2\help.htm, 1116 , 2018-11-22
classification_toolbox_5.2\knnclass.m, 2087 , 2018-11-21
classification_toolbox_5.2\knncv.m, 7771 , 2018-12-04
classification_toolbox_5.2\knnfit.m, 5026 , 2018-12-04
classification_toolbox_5.2\knnksel.m, 4783 , 2018-12-04
classification_toolbox_5.2\knnpred.m, 4446 , 2018-12-04
classification_toolbox_5.2\knn_calc_dist.m, 3841 , 2018-11-21
classification_toolbox_5.2\make_test.m, 3503 , 2018-11-21
classification_toolbox_5.2\mypls.m, 4426 , 2008-10-02
classification_toolbox_5.2\pca_model.m, 3962 , 2018-12-04
classification_toolbox_5.2\pca_project.m, 2564 , 2018-11-21
classification_toolbox_5.2\plsdacompsel.m, 4554 , 2018-12-04
classification_toolbox_5.2\plsdacv.m, 8201 , 2018-12-04
classification_toolbox_5.2\plsdafindclass.m, 1972 , 2018-11-21
classification_toolbox_5.2\plsdafindthr.m, 3236 , 2018-11-21
classification_toolbox_5.2\plsdafit.m, 7931 , 2018-12-04
classification_toolbox_5.2\plsdapred.m, 4254 , 2018-12-04
classification_toolbox_5.2\potcalc.m, 2178 , 2018-11-21
classification_toolbox_5.2\potcv.m, 9456 , 2018-12-04
classification_toolbox_5.2\potfindclass.m, 1993 , 2018-11-21
classification_toolbox_5.2\potfit.m, 5984 , 2018-12-04
classification_toolbox_5.2\potpred.m, 3102 , 2018-12-04
classification_toolbox_5.2\potsmootsel.m, 5634 , 2018-12-04
classification_toolbox_5.2\readme.txt, 3413 , 2018-12-04
classification_toolbox_5.2\redo_scaling.m, 2297 , 2018-11-21
classification_toolbox_5.2\sediment.mat, 107841 , 2018-11-15
classification_toolbox_5.2\simcacompsel.m, 4555 , 2018-12-04
classification_toolbox_5.2\simcacv.m, 8581 , 2019-02-13
classification_toolbox_5.2\simcafindclass.m, 2077 , 2018-11-21
classification_toolbox_5.2\simcafindthr.m, 2720 , 2018-11-21
classification_toolbox_5.2\simcafit.m, 8275 , 2019-02-13
classification_toolbox_5.2\simcapred.m, 4328 , 2019-02-13
classification_toolbox_5.2\svmcostsel.m, 6034 , 2018-12-04
classification_toolbox_5.2\svmcv.m, 8599 , 2018-12-04

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • MSA
    说明:  自动生成Excel表格,包括测量系统分析MSA GR&R--均值极差法 ,方差分析,均值极差(Automatic generation of Excel tables, including measurement system analysis MSA GR&R - mean extreme difference method, variance analysis, mean extreme difference)
    2019-06-20 21:24:10下载
    积分:1
  • Kmeans-python
    聚类分析31省市的经济情况,以每个聚类簇的平均值来衡量省市经济的发展水平。(Cluster analysis of the economic situation of 31 provinces and municipalities, with the average value of each cluster to measure the level of economic development of provinces and municipalities.)
    2020-07-03 13:40:02下载
    积分:1
  • chks光滑支持向量机-程序
    CHKS光滑孪生支持向量机程序, 采用CHKS光滑函数逼近无约束孪生支持向量机的不可微部分,得到一类光滑的孪生支持向量机。(CHKS smooth twin support vector machine program)
    2020-12-25 09:09:04下载
    积分:1
  • Classifiers___Bagging+Knn
    该程序用于分类,用到的算法是Bagging and Knn 两种算法(This program is used for classification, the algorithm used is bagging and knn two algorithms)
    2020-07-02 05:40:02下载
    积分:1
  • boston_housing
    说明:  采用机器学习预测房价.使用波士顿房屋信息数据来训练和测试一个模型,并对模型的性能和预测能力进行评估。(Using Machine Learning to Predict House Prices)
    2019-10-04 11:48:44下载
    积分:1
  • rdqern
    脉动风速功率谱估计,并与标准风谱进行对比()
    2018-05-25 15:20:02下载
    积分:1
  • Archive
    PCA 数据降维 PTYTHON 数据分析/挖掘(PCA dimensionality reduction data mining/analysis)
    2020-06-21 15:40:02下载
    积分:1
  • havz-bhlding
    BP网络VC代码 其实这就是成型的算法,估计好多人写过(BP network VC code is actually a molding algorithm, estimated that a lot of people have written)
    2018-09-06 15:00:59下载
    积分:1
  • FDP聚类算法
    说明:  一种无监督的聚类算法,基于密度聚类,名称为基于快速搜索与寻找密度峰值的聚类(Clustering by fast search and find of desity peaks)
    2020-02-24 15:43:51下载
    积分:1
  • knn.py
    kNN算法的核心思想是如果一个样本在特征空间中的k个最相邻的样本中的大多数属于某一个类别,则该样本也属于这个类别,并具有这个类别上样本的特性。该方法在确定分类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。 kNN方法在类别决策时,只与极少量的相邻样本有关。由于kNN方法主要靠周围有限的邻近的样本,而不是靠判别类域的方法来确定所属类别的,因此对于类域的交叉或重叠较多的待分样本集来说,kNN方法较其他方法更为适合。(Basic source application)
    2018-10-30 16:50:13下载
    积分:1
  • 696516资源总数
  • 106457会员总数
  • 15今日下载