Results 61 to 70 of about 537,050 (171)

Distilling Diverse Knowledge for Deep Ensemble Learning

IEEE Access
Bidirectional knowledge distillation improves network performance by sharing knowledge between networks during the training of multiple networks. Additionally, performance is further improved by using an ensemble of multiple networks during inference ...
Naoki Okamoto, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi   +3 more
doaj   +1 more source

Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification

Complexity, 2018
The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system.
Zeynep H. Kilimci, Selim Akyokus
doaj   +1 more source

Automatic Detection of Students’ Engagement During Online Learning: A Bagging Ensemble Deep Learning Approach

IEEE Access
The COVID-19 pandemic has reshaped education and shifted learning from in-person to online. While this shift offers advantages such as liberating the learning process from time and space constraints and enabling education to occur anywhere and anytime, a
Mayanda Mega Santoni, T. Basaruddin, Kasiyah Junus, Oenardi Lawanto   +3 more
doaj   +1 more source

On Ensemble Learning

, 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.
Stamp, Mark, Chandak, Aniket, Wong, Gavin, Ye, Allen   +3 more
openaire   +2 more sources

Self-poised Ensemble Learning

, 2005
This paper proposes a new approach to train ensembles of learning machines in a regression context. At each iteration a new learner is added to compensate the error made by the previous learner in the prediction of its training patterns. The algorithm operates directly over values to be predicted by the next machine to retain the ensemble in the target
Nanculef, R, Valle, C, Allende, H, Moraga, C   +3 more
openaire   +3 more sources

Predicting Residential Energy Consumption in South Africa Using Ensemble Models

Applied Computational Intelligence and Soft Computing
This study presents ensemble machine learning (ML) models for predicting residential energy consumption in South Africa. By combining the best features of individual ML models, ensemble models reduce the drawbacks of each model and improve prediction ...
David Attipoe, Donatien Koulla Moulla, Ernest Mnkandla, Alain Abran   +3 more
doaj   +1 more source

Sex estimation with ensemble learning: an analysis using anthropometric measurements of piriform aperture

Egyptian Journal of Forensic Sciences
Background Piriform aperture is an anatomical region that has been very little studied in terms of sex estimation. Ensemble learning is similarly an unstudied area in sex estimation from human skeletal remains.
Muhammed Emin Parlak, Yasin Etli, Murat Beyhan, Kubilay Kanat, Hüseyin Alper Kızıloğlu   +4 more
doaj   +1 more source

Ensemble Learning for the Land Cover Classification of Loess Hills in the Eastern Qinghai–Tibet Plateau Using GF-7 Multitemporal Imagery

Remote Sensing
The unique geographic environment, diverse ecosystems, and complex landforms of the Qinghai–Tibet Plateau make accurate land cover classification a significant challenge in plateau earth sciences.
Feifei Shi, Xiaohong Gao, Runxiang Li, Hao Zhang   +3 more
doaj   +1 more source

An adaptive ensemble feature selection technique for model-agnostic diabetes prediction

Scientific Reports
Ensemble learning aggregates several models’ outputs to improve the overall model’s performance. Ensemble feature selection separating the appropriate features from the extra and non-essential features. In this paper, the main focus will be to expand the
K. Natarajan, Dhanalakshmi Baskaran, Selvakumar Kamalanathan   +2 more
doaj   +1 more source

Optimal weighted parameters of ensemble convolutional neural networks based on a differential evolution algorithm for enhancing pornographic image classification

Engineering and Applied Science Research, 2021
Use of ensemble convolutional neural networks (CNNs) has become a more robust strategy to improve image classification performance. However, the success of the ensemble method depends on appropriately selecting the optimal weighted parameters. This paper
Sarayut Gonwirat, Olarik Surinta
doaj