  We propose a Parton Distribution Function (PDF) fitting technique which is
based on an interactive neural network algorithm using Self-Organizing Maps
(SOMs). SOMs are visualization algorithms based on competitive learning among
spatially-ordered neurons. Our SOMs are trained with stochastically generated
PDF samples. On every optimization iteration the PDFs are clustered on the SOM
according to a user-defined feature and the most promising candidates are
selected as a seed for the subsequent iteration. Our main goal is thus to
provide a fitting procedure that, at variance with the global analyses and
standard neural network approaches, allows for an increased control of the
systematic bias by enabling user interaction in the various stages of the
fitting process.
