Sparse Bayesian learning (SBL) is a popular machine learning approach with a superior
generalization capability due to the sparsity of its adopted model. However, it entails a matrix …

Abstract Recently, the Adaptive-Network-Based Fuzzy Inference System (ANFIS) is applied
in many areas of knowledge, and there are multiple optimization algorithms for its learning …

The previous matrix regression based methods mainly focus on designing a robust error
term to characterize the occlusion and illumination changes. In actually, it is very challenging …

Neighborhood preserving embedding (NPE) has been proposed to encode overall
geometry manifold embedding information. However, the class-special structure of the data …

Due to the simplicity and competitive classification performance of the naive Bayes (NB),
researchers have proposed many approaches to improve NB by weakening its attribute …

Due to the nonsparse representation, the use of compressed sensing (CS) for physiological
signals, such as a multichannel electroencephalogram (EEG), has been a challenge. We …

Bayesian networks have long been a popular medium for graphically representing the
probabilistic dependencies which exist in a domain. State-of-the-art tree-augmented naive …

In this paper a generalized matrix variate gamma distribution, which includes a trace
function in the kernel of the density, is introduced. Some important statistical properties …

非接触式人体体温测量具有十分重要的应用价值. 目前, 非接触人体体温测量主要是使用红外
电子体温枪等进行人工测量. 面向人群密集区域, 人工测量很难准确, 快速地完成. 面对上述问题 …

Modelling noisy data in a network context remains an unavoidable obstacle; fortunately,
random matrix theory may comprehensively describe network environments effectively. Thus …