The automatic induction of machine learning models capable of addressing supervised
learning, feature selection, clustering and reinforcement learning problems requires …

Link prediction aims at inferring missing links or predicting future ones based on the
currently observed network. This topic is important for many applications such as social …

The ELVD (Ensemble-based Lenet VGGNet and DropoutNet) model is used in this paper to
examine hypothetical principles and theoretical identification of a real-time image …

Dimension reduction is one of the key data transformation techniques in machine learning
and knowledge discovery. It can be realized by using linear and nonlinear transformation …

Abstract Denoising Autoencoder Genetic Programming (DAE-GP) is a model-based
evolutionary algorithm that uses denoising autoencoder long short-term memory networks …

Denoising autoencoder genetic programming (DAE-GP) is a novel neural network-based
estimation of distribution genetic programming approach that uses denoising autoencoder …

Abstract Denoising Autoencoder Genetic Programming (DAE-GP) is a novel neural network-
based estimation of distribution genetic programming (EDA-GP) algorithm that uses …

Estimation-of-distribution algorithms (EDAs) are optimization algorithms that learn a
distribution from which good solutions can be sampled easily. A key parameter of most …

An additive autoencoder for dimension reduction, which is composed of a serially performed
bias estimation, linear trend estimation, and nonlinear residual estimation, is proposed and …

In the era of big data, feature engineering has proved its efficiency and importance in
dimensionality reduction and useful information extraction from original features. Feature …