An important goal of indoor positioning systems is to improve accuracy as well reduce power consumption. In this paper, we propose an method based on the received signal strength (RSS) fingerprint. The proposed used a certain criterion select fixed access points (FPs) in offline phase instead online for location estimation. Principal component analysis (PCA) was applied features RSS measurement...

Computational efficiency is important for learning algorithms operating in the "large p, small n" setting. In computational biology, the analysis of data sets containing tens of thousands of features ("large p"), but only a few hundred samples ("small n"), is nowadays routine, and regularized regression approaches such as ridge-regression, lasso, and elastic-net are popular choices. In this pap...

The estimation of ridge parameter is an important problem in the ridge regression method, which is widely used to solve multicollinearity problem. A comprehensive study on 28 different available estimators and five proposed ridge estimators, KB1, KB2, KB3, KB4, and KB5, is provided. A simulation study was conducted and selected estimators were compared. Some of selected ridge estimators perform...

We introduce an efficient parallel implementation of a Kernel Ridge Regression solver, based on the Gaussian Belief Propagation algorithm (GaBP). Our approach can be easily used in Peer-to-Peer and grid environments, where there is no central authority that allocates work. Empirically, our solver has high accuracy in solving classification problems. We have tested our distributed implementation...

The stringent quality requirement of petroleum products in highly competitive markets makes on-line controlling of distillation composition essential. In this paper, a novel method using sensitivity matrix analysis and kernel ridge regression to implement on-line estimation of distillation compositions is proposed. In the approach, the sensitivity matrix analysis is presented to select the most...

A procedure for constructing a vector of regression weights is considered. Under the regression superpopulation model, the ridge regression estimator that has minimum model mean squared error is derived. Through a simulation study, the ridge regression weights, regression weights, quadratic programming weights and raking ratio weights are compared. The ridge regression procedure with weights bo...

Although there exist a number of single color constancy algorithms, none of them can be considered universal. Consequently, how to select and combine existing single algorithms are two important research directions in the field of color constancy. In this paper we use ridge regression, a simple yet effective machine learning approach, to select and combine existing color constancy algorithms. T...

In 1996 an Introduction to Radial Basis Function Networks was published on the web 2 along with a package of Matlab functions 3. The emphasis was on the linear character of RBF networks and two techniques borrowed from statistics: forward selection and ridge regression. This document 4 is an update on developments between 1996 and 1999 and is associated with a second version of the Matlab packa...

Abstract: Subset selection regression is a frequently used statistical method. It waives some of the predictor variables and the prediction equation is based on the remaining set of variables. Subset selection is simple and it clearly reduces the variance. An other method for reducing the variance is ridge regression. Usually, subset selection is not as accurate as ridge. The problems with ridg...