Results 41 to 50 of about 537,050 (171)

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

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

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

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

Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses

Bioengineering, 2023
Ultrasound (US) is often used to diagnose liver masses. Ensemble learning has recently been commonly used for image classification, but its detailed methods are not fully optimized.
Norio Nakata, Tsuyoshi Siina
doaj   +1 more source

Markov Network Structure Learning via Ensemble-of-Forests Models [PDF]

, 2013
Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models.
Arvaniti, Eirini, Claassen, Manfred
core  

Statistical Mechanics of Nonlinear On-line Learning for Ensemble Teachers

, 2007
We analyze the generalization performance of a student in a model composed of nonlinear perceptrons: a true teacher, ensemble teachers, and the student.
Freund Y., Hara K., Krogh A., Miyoshi S., Miyoshi S., Miyoshi S., Nishimori H., Saad D., Urakami M., Urbanczik R.   +9 more
core   +1 more source

Foundations and Innovations in Data Fusion and Ensemble Learning for Effective Consensus

Mathematics
Ensemble learning and data fusion techniques play a crucial role in modern machine learning, enhancing predictive performance, robustness, and generalization.
Ke-Lin Du, Rengong Zhang, Bingchun Jiang, Jie Zeng, Jiabin Lu   +4 more
doaj   +1 more source

On-line Learning of an Unlearnable True Teacher through Mobile Ensemble Teachers

, 2008
On-line learning of a hierarchical learning model is studied by a method from statistical mechanics. In our model a student of a simple perceptron learns from not a true teacher directly, but ensemble teachers who learn from the true teacher with a ...
Engel A., Inoue J., Miyoshi S., Miyoshi S., Miyoshi S., Nishimori H., Saad D., Urakami M., Urbanczik R., Utsumi H.   +9 more
core   +1 more source