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Description: Description: 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: Z.-H. Zhou and Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.4, pp.725-735.
-Description: 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: Z.-H. Zhou and Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics, 2005, vol.35, no.4, pp.725-735
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Author: 修宇 |
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Description: Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer.
Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42.
使用神经网络集成方法诊断糖尿病,肝炎,乳腺癌症的案例研究.
-Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer. Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42.
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Author: 修宇 |
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Description: A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS
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Author: 由从哲 |
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Description: 神经网络集成的例子!基于南大周志华的论文，用神经网络集成解决异或问题!-neural network ensemble example! South dragon on the thesis, using neural network integration solutions differences or problems!
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Author: zay |
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Description: Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
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Author: njustyw |
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Description: The program implements three large-margin thresholded ensemble
algorithms for ordinal regression. It includes an improved RankBoost
algorithm, the ORBoost-LR algorithm, and the ORBoost-All algorithm. -The program implements three large-margin thresholded ensemblealgorithms for ordinal regression. It includes an improved RankBoostalgorithm, the ORBoost-LR algorithm, and the ORBoost-All algorithm.
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Size: 13312 |
Author: xandp |
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Description: An+ensemble learning approach to independent component analysis
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Author: 岑松 |
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Description: This toolbox contains re-implementations of four different multi-instance learners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance learners can be built with this toolbox
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Author: wsy |
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Description: NeC4.5 is a variant of C4.5 decision tree, which could generate decision trees more accurate than standard C4.5 decision trees, through regarding a neural network ensemble as a pre-process of C4.5 decision tree.
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Description: 集合卡尔曼滤波(EnKF) 数据同化方法可以避免了EKF 中协方差演变方程预报过程中出现的计算不准确和关于协方差矩阵的大量数据的存储问题,最主要的是可以有效的控制估计误差方差的增长,改善预报的效果。-Ensemble Kalman Filter (EnKF) data assimilation methods can be avoided in the EKF covariance forecasting the evolution equation arising in the course of the calculation is not accurate and on the covariance matrix of a large amount of data storage problems, the most important and effective control can be estimated error variance of the growth, improvement in forecasting results.
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Author: 胡军 |
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Description: matlab格式源代码。功能：模糊BP神经网络集成解耦算法和应用于控制优化模型问题。-matlab source code format. Function: fuzzy BP neural network ensemble decoupling control algorithm and optimization model applied to the problem.
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Author: magic |
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Description: Generation of the ensemble average.
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Author: hecsp |
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Description: ensemble classifier example
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Author: tim |
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Description: 从因子分析的角度出发解决基因表达谱分析问题。为解决独立成分分析方法在求解过程中的不稳定性，提出一种基于选择性独立成分分析的DNA微阵列数据集成分类器。首先对基因表达水平的重构误差进行分析，选择部分重构误差较小的独立成分进行样本重构，然后基于重构后的样本同时训练多个支持向量机基分类器，最后选择部分分类正确率较高的基分类器进行最大投票以得到最终结果。在3个常用测试集上验证了本文设计方法的有效性。-This paper tries to deal with gene expression problem in view of factor analysis. In order to overcome the instability problem caused by performing independent component analysis, a DNA microarray data ensemble classifier based on selective independent component analysis is proposed. The reconstruction error of each gene is analyzed firstly and a part of independent components which contribute relatively small reconstruction errors are selected to reconstruct new samples. After that, several support vector machine base classifiers are trained simultaneously. Finally, the best base classifiers with high correct rates are selected to participate in the ensemble, using the majority voting method. Results on three publicly available microarray datasets show the feasibility and validity of the method proposed in this paper.
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Author: cumtgyy |
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Description: 该程序是用matlab写的一个利用遗传算法的选择性集成算法-The program is written in a matlab genetic algorithm using selective Ensemble Learning algorithm
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Author: eric |
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Description: 聚类分析工具箱 亚历山大博士写的，用于聚类分析，功能比较全-Cluster Analysis and Cluster Ensemble Software
ClusterPack is a collection of Matlab functions for cluster analysis. It consists of the three modules ClusterVisual, ClusterBasics, and ClusterEnsemble as described in the following. They are a selection out of my personal codebase for machine learning research. They contain general clustering algorithms as well as special algorithms developed in my research as indicated in the README files
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Author: 王鹏 |
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Description: The matlab code implements the ensemble of decision tree classifiers proposed in: "L. Nanni and A. Lumini, Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification, Expert Systems With Applications doi:10.1016/j.eswa.2009.02.072 "
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Author: loris nanni |
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Description: 基于自组织数据挖掘的多分类器集成选择的程序-Multiple classifiers ensemble selection based on GMDH
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Author: 肖进 |
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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.
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Author: ldwcuit
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Description: Ensemble clustering for image
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Author: Rah85
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