Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

Abstract The Restricted Boltzmann Machine (RBM) has aroused wide interest in machine
learning fields during the past decade. This review aims to report the recent developments in …

Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …

Entity matching (EM) finds data instances that refer to the same real-world entity. In this
paper we examine applying deep learning (DL) to EM, to understand DL's benefits and …

In the literature of remote sensing, deep models with multiple layers have demonstrated their
potentials in learning the abstract and invariant features for better representation and …

Motivation Properties of molecules are indicative of their functions and thus are useful in
many applications. With the advances of deep-learning methods, computational approaches …

Ensemble learning uses multiple algorithms to obtain better predictive performance than any
single one of its constituent algorithms could. With the growing popularity of deep learning …

Combination therapy is a promising strategy for confronting the complexity of cancer.
However, experimental exploration of the vast space of potential drug combinations is costly …

The hyperspectral image (HSI) classification aims to assign each pixel to a land-cover
category. It is receiving increasing attention from both industry and academia. The main …

Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral
imaging (HSI) has been widely used in a wide range of real-world application areas due to …