Studying heavy metal adsorption on soil is important for understanding the fate of heavy
metals and properly assessing the related environmental risks. Existing experimental …

Least square regression is a very popular supervised classification method. However, two
main issues greatly limit its performance. The first one is that it only focuses on fitting the …

In this paper, we aim at learning compact and discriminative linear regression models.
Linear regression has been widely used in different problems. However, most of the existing …

Linear regression (LR) and some of its variants have been widely used for classification
problems. Most of these methods assume that during the learning phase, the training …

The various variants of the classical least square regression (LSR) have been extensively
utilized in numerous applications. However, most previous linear regression methods only …

Myoelectric signals can be used to predict the intended movements of an amputee for
prosthesis control. However, untrained effects like limb position changes influence …

Ridge regression (RR) and its extended versions are widely used as an effective feature
extraction method in pattern recognition. However, the RR-based methods are sensitive to …

Classification of alcoholic electroencephalogram (EEG) signals is a challenging job in
biomedical research for diagnosis and treatment of brain diseases of alcoholic people. The …

This paper addresses two fundamental problems: 1) learning discriminative model
parameters and 2) avoiding over-fitting, which often occurs in regression-based …

In the machine learning field, especially in classification tasks, the model's design and
construction are very important. Constructing the model via a limited set of features may …