Results 61 to 70 of about 6,303,129 (314)

The Effect of Bioclimatic Covariates on Ensemble Machine Learning Prediction of Total Soil Carbon in the Pannonian Biogeoregion

Agronomy, 2023
This study employed an ensemble machine learning approach to evaluate the effect of bioclimatic covariates on the prediction accuracy of soil total carbon (TC) in the Pannonian biogeoregion. The analysis involved two main segments: (1) evaluation of base
Dorijan Radočaj, Mladen Jurišić, Vjekoslav Tadić   +2 more
doaj   +1 more source

LightGBM: accelerated genomically designed crop breeding through ensemble learning

Genome Biology, 2021
LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize lines.
Jun Yan, Yuetong Xu, Qian Cheng, Shuqin Jiang, Qian Wang, Yingjie Xiao, Chuang Ma, Jianbing Yan, Xiangfeng Wang   +8 more
semanticscholar   +1 more source

Disordered but rhythmic—the role of intrinsic protein disorder in eukaryotic circadian timing

FEBS Letters, EarlyView.
Unstructured domains known as intrinsically disordered regions (IDRs) are present in nearly every part of the eukaryotic core circadian oscillator. IDRs enable many diverse inter‐ and intramolecular interactions that support clock function. IDR conformations are highly tunable by post‐translational modifications and environmental conditions, which ...
Emery T. Usher, Jacqueline F. Pelham
wiley   +1 more source

Diverse Models, United Goal: A Comprehensive Survey of Ensemble Learning

CAAI Transactions on Intelligence Technology
Ensemble learning, a pivotal branch of machine learning, amalgamates multiple base models to enhance the overarching performance of predictive models, capitalising on the diversity and collective wisdom of the ensemble to surpass individual models and ...
Ziwei Fan, Zhiwen Yu, Kaixiang Yang, Wuxing Chen, Xiaoqing Liu, Guojie Li, Xianling Yang, C. L. Philip Chen   +7 more
doaj   +1 more source

Ensemble-based kernel learning for a class of data assimilation problems with imperfect forward simulators.

PLoS ONE, 2019
Simulator imperfection, often known as model error, is ubiquitous in practical data assimilation problems. Despite the enormous efforts dedicated to addressing this problem, properly handling simulator imperfection in data assimilation remains to be a ...
Xiaodong Luo
doaj   +1 more source

Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

Journal of Cheminformatics, 2020
Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure–activity
Chia-Hsiu Chen, Kenichi Tanaka, Masaaki Kotera, Kimito Funatsu   +3 more
doaj   +1 more source

Survey of transformers and towards ensemble learning using transformers for natural language processing

Journal of Big Data
The transformer model is a famous natural language processing model proposed by Google in 2017. Now, with the extensive development of deep learning, many natural language processing tasks can be solved by deep learning methods.
Hongzhi Zhang, M. O. Shafiq
semanticscholar   +1 more source

Optimization of the Asymptotic Property of Mutual Learning Involving an Integration Mechanism of Ensemble Learning

, 2007
We propose an optimization method of mutual learning which converges into the identical state of optimum ensemble learning within the framework of on-line learning, and have analyzed its asymptotic property through the statistical mechanics method.The ...
Breiman L., Freund Y., Hara K., Hara K., Krogh A., Lazarevic A., Metzler R., Mislovaty R., Miyoshi S., Saad D.   +9 more
core   +1 more source

Ensemble Kalman filter for neural network based one-shot inversion

, 2020
We study the use of novel techniques arising in machine learning for inverse problems. Our approach replaces the complex forward model by a neural network, which is trained simultaneously in a one-shot sense when estimating the unknown parameters from ...
Guth, Philipp A., Schillings, Claudia, Weissmann, Simon   +2 more
core   +1 more source

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.
Wiering, M.A., van Hasselt, H.P.
openaire   +3 more sources