搜索资源列表
random-forest
- 随机森林算法,使用c/c++,可直接使用和研究-Random Forest algorithm, using c/c++, can be used directly and research
RF_MexStandalone-v0.02-precompiled
- 随机森林信号分类识别,可以处理非线性离散信号,识别率高,效果好(Random forest signal classification)
RandomForest
- 随机森林分类器算法的应用,包含分类例子和回归例子(Random forest algorithm matlab)
Random-Forest-Matlab-master
- 随机森林的matlab实现,即随机森林算法的matlab工具箱,从GitHub上获取,使用请注明出处。(random forest for matlab)
random_forest
- 随机森林在mnist上的实现,可以下载数据集后,改变路径运行。(Random forest on the MNIST implementation, you can download the data set, change the path to run.)
life
- 森林扩张,生态演替,基于matlab编码,图像显示(Forest expansion, ecological succession, based on MATLAB encoding, image display)
forest
- an enhance algoritm of random forest
Random Forest
- 利用随机森林训练,对polar码进行解码(The polar code is decoded by random forest)
62192
- this is random forest code
pyforest-master
- this is random forest in python
Fuzzy random forest
- Random forest demo; it just a document
random forest-matlab
- rf-随机森林:一个简单的随机森林例子,包含C4.5,ID3等多种信息熵计算过程(A simple example of random forest, including C4.5, ID3 and other information entropy calculation process)
Random Forest
- 在机器学习中,随机森林是一个包含多个决策树的分类器, 并且其输出的类别是由个别树输出的类别的众数而定。 Leo Breiman和Adele Cutler发展出推论出随机森林的算法。 而 "Random Forests" 是他们的商标。 这个术语是1995年由贝尔实验室的Tin Kam Ho所提出的随机决策森林(random decision forests)而来的。这个方法则是结合 Breimans 的 "
RF
- deep learning:random forest
gcForest-master
- 基于决策树构建深度森林模型实现较高特征表示能力相比深度卷积神经网络(Building deep forest model based on decision tree to achieve higher feature representation ability compared with deep convolution neural network)
train the random forest
- train the random forest classifier for digital processing
randomforest.R
- R 语言 随机森林分类特征选择,打分特征重要性(R language random forest feature selection importance)
RF_Class_C
- 随机森林分类算法matlab例程, 内含详细语句说明(Random forest classification algorithm)
随机森林分类器
- 对提取好的n维特征,实现随机森林分类器分类。(For the extraction of good characteristics, the realization of random forest classification)
随即森林
- 这是随机森林的matlab程序包 random forest(Random forest package)