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[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 ...
As the extensive and successful applications of artificial intelligence in manufacturing areas, meta-heuristics and reinforcement learning methods achieve great breakthroughs in addressing manufacturing scheduling problems. It is noted that a hybridization of meta-heuristic and reinforcement learning algorithms ...
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. ...
Ensemble approaches may use either a single base learning algorithm or numerous learning algorithms to Basic idea and system architecture As introduced above, the focus of this paper is to explore the use of ensembles to enable high-performance AI services based on resource-limited edge servers and...
Mirchandani P, Head L (2001) A real-time traffic signal control system: architecture, algorithms, and analysis. Transportation Research Part C: Emerging Technologies 9(6):415–432. https://doi.org/10.1016/S0968-090X(00)00047-4, https://www.sciencedirect.com/science/article/pii/S0968090X00...
we propose a novel reinforcement learning-based method for integrating base learners in sentiment analysis. Our method modifies the influence of base learners on the ensemble output based on the problem space, without requiring prior knowledge of the input domain. This approach effectively manages the...
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 ...