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 ...