Abstract The Extreme Learning Machine is a single-hidden-layer feedforward learning
algorithm, which has been successfully applied in regression and classification problems in …

Abstract Convolutional Neural Networks (CNNs) have achieved potentially good results for
image classification. Due to their learning capabilities such networks are explored and …

As a compact and effective learning model, the random vector functional link neural network
(RVFL) has been confirmed with universal approximation capabilities. It has gained …

Orthogonal transformations, proper decomposition, and the Moore–Penrose inverse are
traditional methods of obtaining the output layer weights for an extreme learning machine …

Autoencoders are neural networks that are characterized by having the same inputs and
outputs. This kind of Neural Networks aim to estimate a nonlinear transformation whose …

This thesis aims to explore the potential of statistical concepts, specifically the Vapnik-
Chervonenkis Dimension (VCD)[33], in optimizing neural networks. With the increasing use …

Anomaly detection plays a pivotal role in numerous real-world scenarios, such as industrial
automation and manufacturing intelligence. Recently, variational inference-based anomaly …

Large features count and minimized sample size of microarray data makes the difficulties for
machine learning researchers. So, feature selection plays a major role in this field and …