Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …

This paper has proposed an effective intelligent prediction model that can well discriminate
and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical …

In the world of ubiquitous computing, fuzzy logic has been emerged as an important
research area in the field of face recognition (FR) applications. In this paper, a new efficient …

Decomposition methods play an important role in solving large-scale quadratic
programming (QP) problems arising from support vector machines (SVMs). In this paper, we …

This article presents an extended Parameterized Fuzzy Semi-supervised learning (PFSL)
method, in which the key innovation is the capability of separating a sample set into two …

To improve the video semantic analysis for video surveillance, a new video semantic
analysis method based on the kernel discriminative sparse representation (KSVD) and …

Dimensionality reduction methods have commonly been used as principled ways to
understand high-dimensional data. In this paper, a novel non-linear method named …

In many practical applications of machine vision, a small number of samples are labeled and
therefore, classification accuracy is low. On the other hand, labeling by humans is a very …

Abstract Canonical Correlation Analysis (CCA) reveals linear correlation relationship
between two feature sets, but fails to discover nonlinear relationship. Kernel CCA (KCCA) …

Dimensionality reduction methods (DR) have been commonly used as a principled way to
understand the high-dimensional data. In this paper, a novel semi-supervised nonlinear …