搜索资源列表
FPRF_scripts
- 随机森林源代码,别人编写,自己还没太搞明白,拿来分享-Random forest-related code, others to edit, used to share
RF-Pathway
- 随机森林源代码,JAVA变异环境,本人对该环境不是太熟悉,特拿来分享-Random forest-related code, others to edit, used to share
RFPP_windows
- 随机森林应用相关源代码,JAVA环境下开发,自己对该环境不是太熟悉,特拿来分享-Random forest-related code, others to edit, used to share
randomforestadk.tar
- 随机森林应用相关源代码,JAVA环境下开发,自己对该环境不是太熟悉,特拿来分享-Random forest-related code, others to edit, used to share
rf
- 随机森林应用相关源代码,JAVA环境下开发,自己对该环境不是太熟悉,特拿来分享-Random forest-related code, others to edit, used to share
RandomForestCversion
- 使用C语言实现的机器学习算法随机森林算法源码-Using C language implementation of machine learning algorithm of random forest algorithm source code
RandomForest
- ID3决策树+随机森林算法生成决策森林,采用投票机制进行决策-The ID3 the+ random forest algorithm to generate decision forests voting mechanism for decision-making training data
ensemb-learning
- 处理非平衡问题的集成方法,基于随机森林的集成学习-Ensemble learning method,which is based on the random forest classifier, to deal with data imbalance problem
RandomForest
- 随机森林是由多棵树组成的分类或回归方法。主要思想来源于Bagging算法,Bagging技术思想主要是给定一弱分类器及训练集,让该学习算法训练多轮,每轮的训练集由原始训练集中有放回的随机抽取,大小一般跟原始训练集相当,这样依次训练多个弱分类器,最终的分类由这些弱分类器组合,对于分类问题一般采用多数投票法,对于回归问题一般采用简单平均法。随机森林在bagging的基础上,每个弱分类器都是决策树,决策树的生成过程中中,在属性的选择上增加了依一定概率选择属性,在这些属性中选择最佳属性及分割点,传统做法
data
- 随机森林算法的构造过程:1、通过给定的原始数据,选出其中部分数据进行决策树的构造,数据选取是”有放回“的过程,我在这里用的是CART分类回归树。 2、随机森林构造完成之后,给定一组测试数据,使得每个分类器对其结果分类进行评估,最后取评估结果的众数最为最终结果-Random Forest algorithm construction process: 1, by a given raw data, which part of the decision tree data structu
learning-to-detect-motion-boundaries
- We propose a learning-based approach for motion boundary detection. Precise localization of motion boundaries is essential for the success of optical fl ow estimation, as motion boundaries correspond to discontinuities of the optical fl ow
Stochastic_Bosque
- matlab 随机森林,可以直接使用,输入为特征矩阵,输出为目标值-Matlab random forest, can be used directly, the input feature matrix, the output value of the target
random-forest
- 基于随机森林的人脸识别,讲解详细,对初学者有很大的帮助- U57FA u4E8E u968F u673A u68EE u6797 u7684 u4EBA u8138 u8BC6 u522B uFF0C u8BB2 u89E3 u8BE6 u7EC6 uFF0C u5BF9 u521D u5B66 u8005 u6709 u5F88 u5927 u7684 u5E2E u52A9
20170106RF_Matlab
- 随机森林指的是利用多棵树对样本进行训练并预测的一种分类器,包括两个方面:数据的随机性选取,以及待选特征的随机选取。-Random forest refers to the use of more than one tree to sample the training and prediction of a classifier, including two aspects: random selection of data, as well as the characteristics of
Ensemble-Learning
- 集成学习将若干基分类器的预测结果进行综合,具体包括Bagging算法和AdaBoost算法;还有随机森林算法,利用多棵树对样本进行训练并预测的一种分类器-Integrated learning integrates the prediction results of several base classifiers, including Bagging algorithm and AdaBoost algorithm and random forest algorithm, using a t
exampleRF
- 随机森林在MATLAB上的实现,并且可以对特征进行重要性排序选择。-Random Forest on MATLAB implementation, and can characteristics in order of importance.
RandomForest
- 机器学习随机森林源码。改变决策树的深度对比分类结果。对鸢尾花数据进行决策树分析-random forest
several-classification-algorithm
- 几种基于Matlab的分类算法研究(自组织神经网络,SOM神经网络,LVQ神经网络,决策树,随机森林算法)-Several classification algorithm based on Matlab research (self-organizing neural network, SOM neural network and LVQ neural network, decision tree, the random forest algorithm)
randomForest_4.6-12.tar
- 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 "Bootstrap aggregating" 想法
Windows-Precompiled-RF_MexStandalone-v0.02-
- 机器学习方法随机森林用于将输入的数据分成不同的类(Machine learning method random forest is used to classify input data into different classes)