  We investigate the performance of a machine learning classification
technique, called the Parzen window, to mitigate the fiber nonlinearity in the
context of dispersion managed and dispersion unmanaged systems. The technique
is applied for detection at the receiver side, and deals with the non-Gaussian
nonlinear effects by designing improved decision boundaries. We also propose a
two-stage mitigation technique using digital back propagation and Parzen window
for dispersion unmanaged systems. In this case, digital back propagation
compensates for the deterministic nonlinearity and the Parzen window deals with
the stochastic nonlinear signal-noise interactions, which are not taken into
account by digital back propagation. A performance improvement up to 0:4 dB in
terms of Q factor is observed.
