Single-output regression is a widely used statistical modeling method to predict an output
based on one or more features of a datapoint. If a dataset has multiple outputs, they can be …

The notion of kernels, recently introduced, has drawn much interest as it allows one to obtain
nonlinear algorithms from linear ones in a simple and elegant manner. This, in conjunction …

Since the introduction of support vector machines, we have witnessed the huge
development in theory, models, and applications of what is so-called kernel-based methods …

We propose a novel approach for network-wide traffic state prediction where the statistical
time series model ARIMA is used to postprocess the residuals out of the fundamental …

This letter proposes a multioutput support vector regression (M-SVR) method for the
simultaneous estimation of different biophysical parameters from remote sensing images …

Machine learning develops intelligent computer systems that are able to generalize from
previously seen examples. A new domain of machine learning, in which the prediction must …

Accurate time series prediction over long future horizons is challenging and of great interest
to both practitioners and academics. As a well-known intelligent algorithm, the standard …

Label distribution covers a certain number of labels, representing the degree to which each
label describes the instance. When dealing with label ambiguity, label distribution could …

Highly accurate interval forecasting of a stock price index is fundamental to successfully
making a profit when making investment decisions, by providing a range of values rather …

This paper addresses the problem of multiple-input multiple-output (MIMO) frequency
nonselective channel estimation. We develop a new method for multiple variable regression …