集成学习是使用一系列学习器进行学习,并使用某种规则把各个学习结果进行整合从而获得比单个学习器更好的学习效果的一种机器学习方法。

VIP内容

在很多真实应用中,数据以流的形式不断被收集得到.由于数据收集环境往往发生动态变化,流数据的分布也会随时间不断变化.传统的机器学习技术依赖于数据独立同分布假设,因而在这类分布变化的流数据学习问题上难以奏效.本文提出一种基于决策树模型重用的算法进行分布变化的流数据学习.该算法是一种在线集成学习方法:算法将维护一个模型库,并通过决策树模型重用机制更新模型库.其核心思想是希望从历史数据中挖掘与当前学习相关的知识,从而抵御分布变化造成的影响.通过在合成数据集和真实数据集上进行实验,我们验证了本文提出方法的有效性.

https://engine.scichina.com/doi/10.1360/SSI-2020-0170

成为VIP会员查看完整内容
1
14

最新内容

Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions.

0
1
下载
预览

最新论文

Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions.

0
1
下载
预览
Top