随机森林

提出一种基于随机森林方法的异常样本（outliers）检测方法。

It introduces an outliers detection method based on random forest .

随机森林是LeoBreiman于2001提出的一个组合分类算法。

Random forest is an ensemble classification methods developed by Leo Breiman in 2001 .

本文引进组合学习算法的新方法随机森林（RandomForest，RF）来选择指标，使得到的指标体系更加客观，更加符合机器学习的特点。

A new ensemble-learning algorithm-Random Forest is applied in this paper . As a consequence , the selected index system is more objective and more suitable for machine learning .

并用随机森林、随机梯度Boosting、支持向量机和人工神经网络四种算法对预留的数据进行了预测，结果表明人工神经网络的预测误差最小。

The predicted errors of preserved 300 records were calculated by random forest , stochastic gradient boosting , support vector and artificial neuron network . The results show that artificial neuron network predict the most precisely .

本论文的具体研究工作包括以下几个方面：1、建立了基于随机森林算法和支持向量机算法的RNA结合蛋白识别的集成算法预测模型。

The details about our works are as follows : Firstly , we built ensemble classifiers based on random forest ( RF ) algorithm and support vector machine ( SVM ) method to identify the RNA-binding proteins by integrating various features .

第四章主要介绍了本文所采用的随机森林算法的基本原理，着重阐述与随机森林定义紧密相关的两种方法：分类回归树（CRAT）与Bagging方法。

In the fourth chapter mostly introduces the basic principles of the random forest algorithm and focuses on two parts closely related with the definition of the random forest : the CART and the Bagging method at first .

利用Burkholder-Davis-Gundy引理和Kolmogorov不等式，讨论了Hilbert空间中一类随机森林发展系统的指数稳定性，给出了指数稳定的充分条件。

Using Burkholder-Davis-Gundy lemma and Kolmogorov inequality , exponential stability of a class of stochastic forest evolution system in Hilbert space is discussed . A sufficient condition of exponential stability is established .

并分别采用随机森林和支持向量机作为数据处理的基本技术筛选反映5类样本本质差异的特征变量，通过分类模型的性能以及PCA和PLS-DA的辅助分析，证明了该方法的有效性。

We use SVM and Random method to select variables that show essential difference among five different kinds of samples in our research . The effectiveness of our method is validated by performance of classification models and assistant analysis of PCA and PLS-DA .

基于随机森林的人脸关键点精确定位方法

Accurate localization of facial feature points based on random forest classifier

随机森林：一种重要的肿瘤特征基因选择法

Random forests : an important feature genes selection method of tumor

基因表达数据判别分析的随机森林方法

The Application of Random Forests for the Classification of Gene Expression Data

网络入侵检测中属性分组的随机森林算法

Random Forests Algorithm with Feature Grouping in Network Intrusion Detection

随机森林针对小样本数据类权重设置

Setting of class weights in random forest for small-sample data

其次利用随机森林建立基金评级模型；

Secondly we construct evaluation model based on random forest .

研究的方法主要包括随机森林算法及遗传算法等。

Research methods include random forest algorithm and genetic algorithm etc.

基于改进随机森林的故障诊断方法研究

Fault diagnosis method based on modified random forests

基于随机森林的文本分类模型研究

Automatic text classification model based on random forest

一种基于随机森林的多视角文本分类方法

Multi View Text Categorization Based on Random Forests

由于随机森林参数较少，泛化能力较好，因此被选作分类器进行学习和预测。

And random forest is chosen as classifier because of its high generalization ability .

结论随机森林算法对基因表达数据的分类研究有较好的判别效果。

Conclusion Random Forests possesses excellent performance in the classification of gene expression data .

基于随机森林的多谱磁共振图像分割

Multi-spectral Magnetic Resonance Image Segmentation Using Random Forests

随机森林及其在色谱指纹中的应用研究

Random Forest and Its Application in Chromatographic Fingerprints

目的探讨随机森林算法在基因表达数据分类研究中的应用。

Objective We investigate the use of random forests for classification of gene expression data .

基于随机森林计算相似性的入侵检测算法

Random Forest Similarity-based Intrusion Detection

最后，将选取的描述符输入支持向量机以及随机森林算法建立模型。

At last , the prediction models were developed using support vector machine and random forest methods .

本文的另一内容是对代谢组学时间序列色谱数据及时间序列随机森林分类算法进行研究，给出了一种与时间序列规律性变化度量相结合的时间序列随机森林算法。

A new time series random forest algorithm combined with regular change measurement of the time series is proposed .

在机器学习领域，随机森林是一种重要和常见的数据挖掘方法。

In the field of machine learning , random forest is an important and common method of data mining .

本文基于随机森林模型，针对其在代谢组学数据分析中的应用，进行了大量的研究。

In this paper , it does a lot of researches on random forest when used to analyze metabolomics data .

最后在重建的训练集上，利用随机森林算法构建可以识别抗性基因的分类器。

Finally , we have built a Random forest classifier on the new training sets to realize the R-gene classification .

当森林中决策树的数目增大，随机森林的泛化误差将趋向一个上界。

The generalization error for forests converges to a limit as the number of trees in the forest becomes large .