Jul 2, 2007 · Abstract: In most papers establishing consistency for learning algorithms it is assumed that the observations used for training are ...

In most papers establishing consistency for learning algorithms it is assumed that the observations used for training are realizations of an i.i.d. process.

In most papers establishing consistency for learning algorithms it is assumed that the observations used for training are realizations of an i.i.d. process.

In this paper we go far beyond this classical framework by showing that support vector machines (SVMs) only require that the data-generating process satisfies a ...

Feb 4, 2022 · Since I am measuring soil moisture and weed coverage at the same kettleholes every date, does it mean that I have dependent(paired?)

We study a class of spectral learning methods with dependent observations, including popular ridge regression, Landweber iteration, spectral cut-off and so ...

The generalization performance of ERM algorithm with strongly mixing observations · Computer Science, Mathematics. Machine-mediated learning · 2009.

Jun 22, 2016 · Hi all, I'd like to run a fixed effects model, dependent variable is continuous (income) and explanatory variable is union status, ...

A new method of implementing Support Vector learning algorithms for classification and regression is presented which deals with problems of over-defined ...

In most papers establishing consistency for learning algorithms it is assumed that the observations used for training are realizations of an i.i.d. process.