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In this paper, we show how the ensemble learning approach can be extended to full(cid:173) covariance Gaussian distributions while remaining computationally ...
Bayesian treatments of learning in neural networks are typically based either on local Gaussian approximations to a mode of the.
Abstract. Bayesian treatments of learning in neural networks are typically based either on local Gaussian approximations to a mode of the.
A neural tree network ensemble model is proposed for classification which is an important research field in data mining and machine learning. Firstly, ...
Jan 1, 1998 · In this paper, we show how the ensemble learning approach can be extended to full-covariance Gaussian distributions while remaining ...
Jul 2, 2024 · In this paper, we aim to address this issue for multilayer bootstrap networks (MBN), a recent unsupervised model, in a way as simple as possible.
Jul 5, 2022 · We compare bagging, boosting, and stacking techniques, and show how methods such as multi-layer stacking can outperform other ensemble techniques and non- ...
In this paper, we propose a novel stacking ensemble regression model (ST-NN) that uses MultiLayer Perceptron's (MLP) neural networks as single learners and MLP ...
Direct methods for multilayer community detection need to find a community structure in the multilayer network from scratch, using information from the multiple ...
Feb 9, 2024 · Ensemble Learning (EL) is a well-succeeded family of predictive algorithms that use several models instead of a single one to predict the ...