I'm trying to work out how to implement some machine learning library to help me find out what the correct weighting for each parameter is in order to make a good decision.
In more detail:
Context: trying to implement a date of publication extractor for html files. This is for news sites, so I don't have a generic date format that I can use. I'm using the parser in dateutil in python, which does a pretty good job. I end up with a list of possible publication dates (all the dates in the html file).
From a set of parameters, such as close tags, words close to the date substring, etc. I sort the list according to likelihood of being the publication date. The weighting for each parameter are somehow educated guesses.
I would like to implement a machine learning algorithm that, after a training period (in which the actual publication date is provided), it determines what the weighting for each parameter should be.
I've been reading the documentation of different machine learning libraries in python (pyML, scikit-learn, pybrain), but I haven't found anything useful. I've also read this and there's a close example with determining if a mushroom is eadible or not.
Note: I'm working in python.
I would very much appreciate your help.