Results 11 to 20 of about 185,151 (349)
Ensemble machine learning approach for electronic nose signal processing [PDF]
, 2022 Electronic nose (e-nose) systems have been reported to be used in many areas as rapid, low- cost, and non-invasive instruments. Especially in meat production and processing, e-nose system is a powerful tool to process volatile compounds as a unique ...
Farah Afian +4 more
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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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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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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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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 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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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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Online learning with ensembles [PDF]
Physical Review E, 2000 Supervised online learning with an ensemble of students randomized by the choice of initial conditions is analyzed. For the case of the perceptron learning rule, asymptotically the same improvement in the generalization error of the ensemble compared to the performance of a single student is found as in Gibbs learning. For more optimized learning rules,
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