Description: 由于这段时间一直在学习ADABOOST，还在学习阶段，这是本人学习中所汇总的相关资料，希望会对各位有所帮助-Because this period has been learning ADABOOST, still learning stage, this is my summary of the study by the relevant information, I hope you will help Platform: |
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Author:朱朱 |
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Description: 机器学习是人工智能的一个子领域，是人工智能中非常活跃且范围甚广的主要核心研究领域之一，也是现代智能系统的关键环节和瓶颈。机器学习吸取了人工智能、概率统计、计算复杂性理论、控制论、信息论、哲学、生理学、神经生物学等学科的成果，主要关注于开发一些让计算机可以自动学习的技术，并通过经验提高系统自身的性能。本文介绍了机器学习的概念、基本结构和发展，以及各种机器学习方法，包括机械学习、归纳学习、类比学习、解释学习、基于神经网络的学习以及知识发现等，并简单叙述了机器学习的相关算法，包括决策树算法、随机森林算法、人工神经网络算法、SVM算法、Boosting与Bagging算法、关联规则算法、贝叶斯学习算法以及EM算法等，最后还指出了机器学习的应用及其发展趋势。-Machine learning is a subset of the field of Artificial Intelligence,is very active in Artificial Intelligence and a wide range of research in the field of one of the main core, is the key and the bottleneck of a modern intelligent system.Machine Learning has absorbed the results of Artificial Intelligence, Probability and Statistics, Computational Complexity Theory, Cybernetics, Information Theory, Philosophy, Physiology and Neurobiology, and other disciplines.It concerns mainly about how to develop some of technology which can make the computer auto-learning,and through experiences to improve the performance of the system itself.This paper introduces the concept,the basic structure and development of machine learning, and a variety of machine learning methods,including machine learning, inductive learning, learning by analogy, explanation-based learning,based on neural network learning and knowledge discovery, and so on. And briefly descrip the algorithms of machine learning,includin Platform: |
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Description: 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 in a directory in the Matlab search path, for example, C:\Matlab6p5\work.
You find in the folder a document who show how to install the toolbox. Enjoy ! Platform: |
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Author:Mus |
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Description: 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 analyzing the error function of neural network ensembles, in which individual neural networks are actively guided to learn diversity. By decomposing the ensemble error function, error correlation terms are included in the learning criterion function of individual networks. And all the individual networks in the ensemble are leaded to learn diversity through cooperative training. The method is applied in fault diagnosis of power transformer based on Dissolved Gas Analysis. Experiment results show that, the algorithm has higher accuracy than IEC method and BP network. And the performance is more stable than conventional ensemble method, i.e., Bagging and Boosting. Platform: |
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Author:刘茂 |
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Description: 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 analyzing the error function of neural network ensembles, in which individual neural networks are actively guided to learn diversity. By decomposing the ensemble error function, error correlation terms are included in the learning criterion function of individual networks. And all the individual networks in the ensemble are leaded to learn diversity through cooperative training. The method is applied in fault diagnosis of power transformer based on Dissolved Gas Analysis. Experiment results show that, the algorithm has higher accuracy than IEC method and BP network. And the performance is more stable than conventional ensemble method, i.e., Bagging and Boosting. Platform: |
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Description: 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. Platform: |
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Author:刘茂 |
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Description: 当弱分类器算法使用简单的分类方时,boosting的效果明显地统一地比bagging要好.当弱分类器算法使用C4.5时,boosting比bagging较好,但是没有前者的比较来得明显.-When the weak classifier algorithm using simple classification method, boosting the effect clearly uniformly better than bagging.
When the weak classifier algorithm C4.5 when used, boosting ratio bagging better, but without the former Comparative more obvious. Platform: |
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Author:王孟贤 |
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Description: Contrary to bagging, boosting dynamica lly tries to gen-erate comple mentary lea rners by training the next lea rner
on the inaccuracies of the learner in the preceding iteration. Platform: |
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Author:jj |
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Description: AdaBoost元算法属于boosting系统融合方法中最流行的一种，说白了就是一种串行训练并且最后加权累加的系统融合方法。
具体的流程是：每一个训练样例都赋予相同的权重，并且权重满足归一化，经过第一个分类器分类之后，
计算第一个分类器的权重alpha值，并且更新每一个训练样例的权重，然后再进行第二个分类器的训练，相同的方法.......
直到错误率为0或者达到指定的训练轮数，其中最后预测的标签计算是各系统*alpha的加权和，然后sign（预测值）。
可以看出，训练流程是串行的，并且训练样例的权重是一直在变化的，分错的样本的权重不断加大，正确的样本的权重不断减小。
AdaBoost元算法是boosting中流行的一种，还有其他的系统融合的方法，比如bagging方法以及随机森林。
对于非均衡样本的处理，一般可以通过欠抽样(undersampling)或者过抽样(oversampling)，欠抽样是削减样本的数目，
过抽样是重复的选取某些样本，最好的方法是两种进行结合的方法。
同时可以通过删除离决策边界比较远的样例。
-AdaBoost boosting systems dollar fusion algorithm is the most popular one, it plainly systems integration approach is a serial train and final weighted cumulative.
Specific process is: Each training example is given equal weight, and the weights satisfy normalization, after the first classifiers after
Calculating a first classifier weights alpha value for each sample and updates right weight training, and then the second classifier training, the same way .......
0, or until the specified error rate training rounds, wherein the label is the calculation of the final prediction system* alpha weighted and then sign (predicted value).
As can be seen, the training process is serial, and weight training examples is always changing, the right of the wrong sample weight continued to increase, the right to correct sample weight decreasing.
AdaBoost algorithm is an element, as well as other methods of boosting popular systems integration, such as bagging and random forest method.
For Platform: |
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Author:iihaozl |
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Description: 在2006年9月召开的ICDM会议上，邀请了ACM KDD创新大奖(InnovationAward)和
Top 10 Algorithms in Data Mining
IEEEICDM研究贡献奖(Research Contributions Award)的获奖者们来参与数据挖掘10大算
法的选举，每人提名10种他认为最重要的算法-Classification，Statistical Learning，Top 10 Algorithms in Data Mining，materials on Association Analysis，Link Mining，Clustering，Bagging and Boosting，Sequential Patterns，Integrated Mining，Rough Sets，Graph Mining Platform: |
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Author:yz |
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Description: This book provides researchers, students and practitioners with an introduction to ensemble methods. The book consists of eight chapters which naturally constitute three parts. Platform: |
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Author:ldwcuit
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