van Hasselt, "Ensemble algorithms in reinforce- ment learning," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 38, no. 4, pp. 930-936, 2008.Marco A Wiering and Hado van Hasselt. Ensemble algorithms in reinforcement learning. Systems, Man, and Cybernetics, ...
In this paper, we present a set of algorithms that explicitly incorporate ensemble diversity, a known factor influencing predictive performance of ensembles, into a reinforcement learning framework for ensemble selection. We rigorously tested these approaches on several challenging problems and associated ...
You already developed some algorithms and codes.Lazy to modify them.Ensemble: improving your machine with lit... 查看原文 李宏毅机器学习课程笔记10:Ensemble、Deep Reinforcement Learning 台湾大学李宏毅老师的机器学习课程是一份非常好的ML/DL入门资料,李宏毅老师将课程录像上传到了YouTube,地址:NTUEE ML ...
[机器学习入门] 李宏毅机器学习笔记-35(Ensemble;集成方法) PDF VIDEO Ensemble 俗称打群架,想要得到很好的performance,基本都要用这一手。 You already developed some algorithms and codes.Lazy to modify them.Ensemble: improving your machine with lit... ...
Multi-class imbalanced data sets have been pervasively observed in many real world applications. Many typical machine learning algorithms pose many difficulties dealing with these kinds of data sets. In this paper, we proposed an ensemble pruning approach which is based on Reinforcement Learning ...
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A. Neural network ensembles: evaluation of aggregation algorithms. Artif. Intell. 163, 139–162 (2005). Article MathSciNet MATH Google Scholar Liu, Y. & Yao, X. Ensemble learning via negative correlation. Neural Netw. 12, 1399–1404 (1999). Article Google Scholar Lee, S. et al. ...
Techopedia Explains Ensemble Learning Many ensemble learning tools can be trained to produce various results. Individual algorithms may be stacked on top of each other, or rely on a “bucket of models” method of evaluating multiple methods for one system. In some cases, multiple data sets are ...
(VMs) is an important challenge in the cloud environment. Most of the existing methods focus on using heuristic evolutionary algorithms to solve the optimized assign problem, which is not suitable to the time-sensitive workloads due to the high computation costs. To address this issue, we ...
Algorithms with several paradigms (such as rule-based methods, game theory and reinforcement learning) have achieved great success in solving imperfect information games (IIGs). However, agents based on a single paradigm tend to be brittle in certain aspects due to the paradigm’s weaknesses. In ...