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FASBIR
- Descr iption: FASBIR(Filtered Attribute Subspace based Bagging with Injected Randomness) is a variant of Bagging algorithm, whose purpose is to improve accuracy of local learners, such as kNN, through multi-model perturbing ensemble. Reference:
bagging-and-boosting-NNE
- 主要是给新手熟悉bagging和boosting算法在虹膜中的运用。-bagging and boosting algorithm in the application of the iris.
BoostingandBagging
- boosting算法和bagging算法综述-boosting algorithm and bagging Algorithms
Bagging_predictors
- 介绍Bagging最早的、最经典的文献，作者是Bagging和随机森林的创始人LEO BREIMAN-Bagging first introduced, the most classic literature, the author is the founder of Bagging and random forests LEO BREIMAN
MatlabRandomForest
- MatlabRandomForest is a powerfull toolbox for programing Randim forest, Bagging, Boosting,.., in Matlab. The Matlab functions (RFClass.m, RFReg.m and RFPrint.m) and compiled Fortran code (RFClassification.dll and RFRegression.dll) must be stored i
OCD--code
- 通过对集成误差公式的理论分析，提出了一种能主动引导个体网络进行差异性学习的集成网络学习算法。该方法通过对集成误差的分解，使个体网络的训练准则函数中包含个体网络误差相关度的因素，并通过协同训练，引导个体网络进行差异性学习。该方法在基于油气分析的变压器故障诊断的实验结果表明，该方法的故障诊断准确率优于传统的三比值法与BP神经网络，其性能也比经典的集成方法Bagging和Boosting方法更稳定可靠。-A learning algorithm is proposed in this paper by
ADL-code
- 通过对集成误差公式的理论分析，提出了一种能主动引导个体网络进行差异性学习的集成网络学习算法。该方法通过对集成误差的分解，使个体网络的训练准则函数中包含个体网络误差相关度的因素，并通过协同训练，引导个体网络进行差异性学习。该方法在基于油气分析的变压器故障诊断的实验结果表明，该方法的故障诊断准确率优于传统的三比值法与BP神经网络，其性能也比经典的集成方法Bagging和Boosting方法更稳定可靠。-A learning algorithm is proposed in this paper by
random-subspace-classifier-ensemble
- 随机子空间集成分类器, 可以实现比bagging 更好的分类和识别-Random subspace classifier ensemble
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
NLDA
- 利用matlab实现基于NLDA的人脸识别算法，如有需求做RLDA和bagging NLDA并利用sum rule或者majority rule请参照http://shop.zbj.com/14563255/sid-1213623.html-Matlab codes for NLDA, bagging NLDA, random sampling LDA, Integrating Random Subspace and Bagging for LDA Based Face Recognition
bagging-NLDA-and-RLDA
- 利用matlab实现NLDA人脸识别算法，更详细的random sampling LDA, bagging NLDA和整合LDA算子利用majority vote 和sum rule的matlab 代码，人脸库使用ORL库或者XM2VTS库，地址：http://shop.zbj.com/14563255/sid-1213623.html- matlab codes for NLDA face detection, the face s are ORL. More details about r
Ensemble Methods Foundations and Algorithms
- This book provides researchers, students and practitioners with an introduction to ensemble methods. The book consists of eight chapters which naturally constitute three parts.
bagging_svm
- bagging_svm MATLAB实现(the matlab code of bagging and svm .)
机器学习之随机森林
- Bagging是并行式集成学习方法最著名的代表，Bagging通常对分类任务使用简单投票法，随机森林（RF）是Bagging的一个扩展变体，RF在以决策树为基学习器构建Bagging 集成的基础上，进一步在决策树的训练过程中引入了随机属性选择。在RF中，集成模型的每棵树构建时所需的样本都是由训练集经过有放回的随机抽样得来（即自助采样法bootstrap sample）。