搜索资源列表
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
vbhmm
- Ensemble Learning for Hidden Markov Models.
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
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
Cluster
- 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
adaptive_adaboosting
- 集成学习中，自适应adaboost算法，可以下载下来参考一下-ensemble learning, adaptive adaboost algorithm, can download the reference
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
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
arimanet
- ARIMA模型全称为自回归积分滑动平均模型(Autoregressive Integrated Moving Average Model,简记ARIMA)，是由博克思(Box)和詹金斯(Jenkins)于70年代初提出一著名时间序列预测方法[1] ，所以又称为box-jenkins模型、博克思-詹金斯法。其中ARIMA（p，d，q）称为差分自回归移动平均模型，AR是自回归， p为自回归项； MA为移动平均，q为移动平均项数，d为时间序列成为平稳时所做的差分次数。所谓ARIMA模型，是指将非平稳