This review discussed the dilemma of small data faced by materials machine learning. First,
we analyzed the limitations brought by small data. Then, the workflow of materials machine …

As the prevalence of social media on the Internet, opinion mining has become an essential
approach to analyzing so many data. Various applications appear in a wide range of …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

Multiparametric magnetic resonance imaging (mpMRI) has been shown to improve
radiologists' performance in the clinical diagnosis of breast cancer. This machine learning …

Support vector machine (SVM) is an optimal margin based classification technique in
machine learning. SVM is a binary linear classifier which has been extended to non-linear …

Background Deep learning methods for radiomics/computer‐aided diagnosis (CAD x) are
often prohibited by small datasets, long computation time, and the need for extensive image …

Accurate tool condition monitoring (TCM) is essential for the development of fully automated
milling processes. However, while considerable research has been conducted in industrial …

Objective. Processing strategies are analyzed with respect to the classification of
electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor …

In many machine learning algorithms, a major assumption is that the training and the test
samples are in the same feature space and have the same distribution. However, for many …

One of the biggest challenges of the current big data landscape is our inability to pro-cess
vast amounts of information in a reasonable time. In this work, we explore and com-pare two …