This paper investigates the asymptotic behavior of the soft-margin and hard-margin support
vector machine (SVM) classifiers for simultaneously high-dimensional and numerous data …

Linear discriminant analysis (LDA) is a popular classifier that is built on the assumption of
common population covariance matrix across classes. The performance of LDA depends …

This paper establishes a phase transition for convergence of the hard-margin support vector
machines (SVM) in high dimensional and numerous data regime, drawn from a Gaussian …

In standard discriminant analysis, data are commonly assumed to follow a Gaussian
distribution, a condition which is often violated in practice. In this work, to account for …

In many real classification problems where a limited number of training samples is available,
the linear classifiers based on discriminant analysis are unable to deliver accurate results …

Linear discriminant analysis (LDA) is a notable classification algorithm thanks to its major
success in many applications of the real-world. In spite of its successfulness for low …

Linear discriminant analysis (LDA) based classifiers tend to falter in many practical settings
where the training data size is smaller than, or comparable to, the number of features. As a …

The fight against cancers can greatly benefit from diagnostic methods that provide an array
of accurate minimally invasive tests that can be used for screening. In addition to genetic …

In this dissertation, the design and fabrication of deep-ultraviolet photodetectors were
investigated based on gallium oxide and its alloys, through the heterogeneous integration …

Biomedical signals–based human control systems have been studied in the biomedical field
to improve quality of life. The muscle signal—electromyography (EMG)—is one of the main …