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ensemblelearning
- 该源代码主要是利用bagging,adboosting等集成学习的方法进行图像融合处理，效果甚好！-The main source is the use of bagging, adboosting ensemble learning methods such as image fusion, the effect is very good!
coforest
- CoForest是一种半监督算法，处理集成学习及利用大量未标记数据得到更优越性能的假设。-CoForest is a semi-supervised algorithm, which exploits the power of ensemble learning and large amount of unlabeled data available to produce hypothesis with better performance.
Gasen
- 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
ClusterPackV10
- 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 t
ClustererEnsemble
- 聚类集成的matlab程序,将集成学习算法引入聚类算法中，提高聚类算法的性能-cluster ensemble learning algorithom ,this algorithom put ensemble learning algorithom into the cluster algorithom to improve the capability .
UDEED
- 机器学习大牛周志华教授的关于“整体学习”的文章算法的实现。整体学习可以提高学习机器的推广能力。-Ensemble learning aims to improve generalization ability by using multiple base learners. It is well-known that to construct a good ensemble, the base learners should be accurate as well as divers
nsembgorithm
- 一种多层次选择性集成学习算法A multi-level ensemble learning algorithm-A multi-level ensemble learning algorithm
vbhmm
- Ensemble Learning for Hidden Markov Models.
cPP-TSP-Ensemble
- 7个c++版TSP问题,收集到的可用版本。方便大家的学习和使用！-7 c++ version of the TSP problem, collect the available versions. Facilitate learning and use!
Ensemble-learning-based-on-GMDH
- 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
RSE-v1
- RSE (Regularized Selective Ensemble) is a selective ensemble learning algorithm for binay classification, which constructs ensemble under the regularization framework. In current version, the graph Laplacian serves as the regularizer, and unlabeled d
multi-class-problem
- 将多类别问题分解成多个二类别问题是解决多类别分类问题的常用方式。传统one against all（OAA）分解方式的性能更多的依赖于个体分类器的精度，而不是它的差异性。本文介绍一种基于集成学习的适于多类问题的神经网络集成模型，其基本模块由一个OAA方式的二类别分类器和一个补充多类分类器组成。测试表明，该模型在多类问题上比其他经典集成算法有着更高的精度，并且有较少存储空间和计算时间的优势。-Decompose multi-class problem into multiple binary cl
clusterensemble
- 聚类集成学习方法，提高算法的健壮性，聚类效果比一般算法好-Clustering ensemble learning methods to improve the robustness of the algorithm, the clustering effect is better than the general algorithm
ensemble-learing-for-decision-tree
- 决策树的集成学习,用Java语言实现！具有良好的分类性能！-ensemble learning for decision tree
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
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
hi-ensemble-1.06
- 很好的集成学习代码，包括数据，适合初学者-Good ensemble learning code, including data, suitable for beginners
7----Co-training-A-SEMI-SUPERVISED-ENSEMBLE-LEARN
- 7 - Co-training A SEMI-SUPERVISED ENSEMBLE LEARNING ALGORITHM-6-7 - Co-training A SEMI-SUPERVISED ENSEMBLE LEARNING ALGORITHM-6
ensemble
- Ensemble clustering for image
EnsembleSVM-master
- ensemble learning in SVM