  We present a novel approach to estimate the time delay between light curves
of multiple images in a gravitationally lensed system, based on Kernel methods
in the context of machine learning. We perform various experiments with
artificially generated irregularly-sampled data sets to study the effect of the
various levels of noise and the presence of gaps of various size in the
monitoring data. We compare the performance of our method with various other
popular methods of estimating the time delay and conclude, from experiments
with artificial data, that our method is least vulnerable to missing data and
irregular sampling, within reasonable bounds of Gaussian noise. Thereafter, we
use our method to determine the time delays between the two images of quasar
Q0957+561 from radio monitoring data at 4 cm and 6 cm, and conclude that if
only the observations at epochs common to both wavelengths are used, the time
delay gives consistent estimates, which can be combined to yield 408\pm 12
days. The full 6 cm dataset, which covers a longer monitoring period, yields a
value which is 10% larger, but this can be attributed to differences in
sampling and missing data.
