We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible sequential nature) of training data arising in reinforcement learning we employ a subspace based variant of LS-SVM that sequentially processes the data and is hence especially suited for online learning. This approach is adapte...

Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very lar...

in his insightful editorial, nir eyal explores the connections between nudging and shaming. one upshot of his argument is that we should question the principle of the least restrictive alternative in public health and health policy. in this commentary, i maintain that eyal’s argument undermines only a rather implausible version of the principle of the least restrictive alternative and i sketch ...

fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. several methods for evaluating fuzzy coefficients in linear regression models have been proposed. the first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. in this the...

a weighted linear regression model with impercise response and p-real explanatory variables is analyzed. the lr fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. a least square solution for estimating the parameters of the model is derived. the result are illustrated by the means of some case studies.

soil conservation service (scs) adjusted the kostiakovs infiltration model by adding a constant coefficient, for improvement of estimation. the improved model has three constant parameters, which are difficult to calculate. so scs has taken the third constant parameter as equal to 0.65-0.7 cm to simplify the estimation. this parameter varies in different soil and often outranges the scs estimat...