Results 11 to 20 of about 545,363 (263)
Ensemble reinforcement learning: A survey
Applied Soft Computing, 2023 34 ...
Yanjie Song 0001 +6 more
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Classification Uncertainty Minimization-based Semi-supervised Ensemble Learning Algorithm [PDF]
Jisuanji kexue, 2023 Semi-supervised ensemble learning(SSEL) is a combinatorial paradigm by fusing semi-supervised learning and ensemble learning together,which improves the diversity of ensemble learning by introducing unlabeled samples and at the same time solves the ...
HE Yulin, ZHU Penghui, HUANG Zhexue, Fournier-Viger PHILIPPE
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Argumentation based joint learning: a novel ensemble learning approach. [PDF]
PLoS ONE, 2015 Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ...
Junyi Xu, Li Yao, Le Li
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Mixed Ensemble Learning In Extracurricular Student Of SMPN 15 Palembang
JSM (Jurnal Seni Musik), 2021 This study aims to describe mixed ensemble learning in extracurricular activities in class IX students of SMP Negeri 15 Palembang. The research method used is descriptive qualitative with data collection techniques in the form of observation, interviews,
Gilank Yonsutrisno +2 more
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Ensembles of Learning Machines [PDF]
, 2002 Ensembles of learning machines constitute one of the main current directions in machine learning research, and have been applied to a wide range of real problems. Despite of the absence of an unified theory on ensembles, there are many theoretical reasons for combining multiple learners, and an empirical evidence of the effectiveness of this approach ...
G. VALENTINI, MASULLI, FRANCESCO
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Ensemble Algorithms in Reinforcement Learning [PDF]
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008 This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms.
Marco A. Wiering, Hado van Hasselt
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Short-Term Load Forecasting Based on Multi-Scale Ensemble Deep Learning Neural Network
IEEE Access, 2023 High-precision load forecasting is crucial for the power system planning and electricity market transactions. Recently, deep learning models have been widely used due to their powerful data mining capabilities. However, the existing research mainly focus
Qin Shen +5 more
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Ensemble - an E-Learning Framework [PDF]
J. Univers. Comput. Sci., 2013 JUCS - Journal of Universal Computer Science Volume Nr.
Queirós, Ricardo, Leal, José Paulo
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A review on rainfall forecasting using ensemble learning techniques
e-Prime: Advances in Electrical Engineering, Electronics and Energy, 2023 Significant challenges to human health and life have arisen as a result of heavy rains. Floods and other natural disasters that affect people all over the world every year are caused by prolonged periods of heavy rainfall. Predictions of rainfall must be
Saranagata Kundu +5 more
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, 2020 In this paper, we consider ensemble classifiers, that is, machine learning based classifiers that utilize a combination of scoring functions. We provide a framework for categorizing such classifiers, and we outline several ensemble techniques, discussing how each fits into our framework.
Mark Stamp 0001 +3 more
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