This letter investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by general, we mean that many stationary stochastic processes can be included. We show that when the stochastic processes satisfy a generalized Bernstein-type inequality, a unified treatment on analyzing the learning schemes with various mixing processes ca...

In view of the remarkable time–frequency property obtained fromwavelet packets and the excellent generalization ability derived from the least squares support vector machine (LS SVM), a novel approach is proposed, which focuses on the research on state detection for pressure vessels. The minimum entropy criterion is adopted to realize the optimal wavelet packet decomposition, the feature vector...

Diabetes Mellitus is a major health problem all over the world. Many classification algorithms have been applied for its diagnoses and treatment. In this paper, a hybrid algorithm of Modified-Particle Swarm Optimization and Least SquaresSupport Vector Machine is proposed for the classification of type II DM patients. LS-SVM algorithm is used for classification by finding optimal hyper-plane whi...

Nursing narratives are an important part of patient documentation, but the possibilities to utilize them in the direct care process are limited due to the lack of proper tools. One solution to facilitate the utilization of narrative data could be to classify them according to their content. In this paper, we addressed two issues related to designing an automated classifier: domain experts' agre...

This study develops a least squares support vector machines (LS-SVM) based model for bivariate process to diagnose abnormal patterns of process mean vector, and to help identify abnormal variable(s) when Shewhart-type multivariate control charts based on Hotelling’s T 2 are used. On the basis of studying and defining the normal/abnormal patterns of the bivariate process mean shifts, a LS-SVM pa...

Given a compact metric space X and a strictly positive Borel measure ν on X , Mercer’s classical theorem states that the spectral decomposition of a positive self-adjoint integral operator Tk : L2(ν) → L2(ν) of a continuous k yields a series representation of k in terms of the eigenvalues and -functions of Tk. An immediate consequence of this representation is that k is a (reproducing) kernel a...