Ensemble classification and regression-recent developments, applications and future directions
Ensemble methods use multiple models to get better performance. Ensemble methods have
been used in multiple research fields such as computational intelligence, statistics and …
been used in multiple research fields such as computational intelligence, statistics and …
A review on neural networks with random weights
In big data fields, with increasing computing capability, artificial neural networks have shown
great strength in solving data classification and regression problems. The traditional training …
great strength in solving data classification and regression problems. The traditional training …
Towards understanding ensemble, knowledge distillation and self-distillation in deep learning
We formally study how ensemble of deep learning models can improve test accuracy, and
how the superior performance of ensemble can be distilled into a single model using …
how the superior performance of ensemble can be distilled into a single model using …
Fuzziness based semi-supervised learning approach for intrusion detection system
Countering cyber threats, especially attack detection, is a challenging area of research in the
field of information assurance. Intruders use polymorphic mechanisms to masquerade the …
field of information assurance. Intruders use polymorphic mechanisms to masquerade the …
A comprehensive evaluation of random vector functional link networks
With randomly generated weights between input and hidden layers, a random vector
functional link network is a universal approximator for continuous functions on compact sets …
functional link network is a universal approximator for continuous functions on compact sets …
Modeling ultrasonic welding of polymers using an optimized artificial intelligence model using a gradient-based optimizer
In this study, a new hybrid artificial intelligence approach is proposed to model the ultrasonic
welding of a polymeric material blend. The proposed approach is composed of an ensemble …
welding of a polymeric material blend. The proposed approach is composed of an ensemble …
Randomness in neural networks: an overview
Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …
A survey of randomized algorithms for training neural networks
As a powerful tool for data regression and classification, neural networks have received
considerable attention from researchers in fields such as machine learning, statistics …
considerable attention from researchers in fields such as machine learning, statistics …
Why m heads are better than one: Training a diverse ensemble of deep networks
Convolutional Neural Networks have achieved state-of-the-art performance on a wide range
of tasks. Most benchmarks are led by ensembles of these powerful learners, but ensembling …
of tasks. Most benchmarks are led by ensembles of these powerful learners, but ensembling …
Diversity in machine learning
Machine learning methods have achieved good performance and been widely applied in
various real-world applications. They can learn the model adaptively and be better fit for …
various real-world applications. They can learn the model adaptively and be better fit for …