  We present a four-category classification algorithm for the solar wind, based
on Gaussian Process. The four categories are the ones previously adopted in Xu
& Borovsky [2015]: ejecta, coronal hole origin plasma, streamer belt origin
plasma, and sector reversal origin plasma. The algorithm is trained and tested
on a labeled portion of the OMNI dataset. It uses seven inputs: the solar wind
speed $V_{sw}$, the temperature standard deviation $\sigma_T$, the sunspot
number $R$, the $f_{10.7}$ index, the Alfven speed $v_A$, the proton specific
entropy $S_p$ and the proton temperature $T_p$ compared to a velocity-dependent
expected temperature. The output of the Gaussian Process classifier is a four
element vector containing the probabilities that an event (one reading from the
hourly-averaged OMNI database) belongs to each category. The probabilistic
nature of the prediction allows for a more informative and flexible
interpretation of the results, for instance being able to classify events as
'undecided'. The new method has a median accuracy larger than $90\%$ for all
categories, even using a small set of data for training. The Receiver Operating
Characteristic curve and the reliability diagram also demonstrate the excellent
quality of this new method. Finally, we use the algorithm to classify a large
portion of the OMNI dataset, and we present for the first time transition
probabilities between different solar wind categories. Such probabilities
represent the 'climatological' statistics that determine the solar wind
baseline.
