The estimation of sediment yield concentration is crucial for the development of stream
ventures, watershed management, toxins estimation, soil disintegration, floods, and so on. In …

In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function
(RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm …

Standard SVM training has O (m3) time and O (m2) space complexities, where m is the
training set size. It is thus computationally infeasible on very large data sets. By observing …

Support vector machine (SVM) is a popular pattern classification method with many diverse
applications. Kernel parameter setting in the SVM training procedure, along with the feature …

Support vector machine (SVM) is a supervised machine learning approach that was
recognized as a statistical learning apotheosis for the small-sample database. SVM has …

Support vector machine (SVM) is a novel pattern classification method that is valuable in
many applications. Kernel parameter setting in the SVM training process, along with the …

A novel twin parametric-margin support vector machine (TPMSVM) for classification is
proposed in this paper. This TPMSVM, in the spirit of the twin support vector machine …

A fast Newton method, that suppresses input space features, is proposed for a linear
programming formulation of support vector machine classifiers. The proposed stand-alone …

This study addresses how visual food cues are encoded in reward related brain areas and
whether this encoding might provide information to differentiate between patients suffering …

Purpose: Classic statistical and machine learning models such as support vector machines
(SVMs) can be used to predict cancer outcome, but often only perform well if all the input …