Jason Rennie wrote:
Looks like the format already has formats for some popular models, including
SVM, regression, NNs.
Unclear to me how anyone could prevent us from using the standard unless it
were patented.
Exactly. Usually in a truly open standard, the companies and
individuals that contribute waive any patent rights on the
standard. Given what you can get patents on these days, there
might well be some protected IP lurking there.
It's just strange that an organization with members like this
does not provide very clear and up-front statements about their
IP/licensing policy (or none that I could find, anyway).
I'm probably overreacting. All I'm trying to say is: before
you spend a lot of time on this, find out what the deal is.
--Thilo
> Copyright only protects works of art, which would include
specific PMML files, but not the format. One thing I noticed is that open
source projects are allowed to take part in the PMML process for free...
My interpretation of PMML is that it represents a model. As others have
mentioned, prediction models (e.g. classification, regression; not
clustering) basically have two parts: (1) learning, where the training data
is used to train (optimize parameters for) the model, (2) prediction, where
values are assigned to data points (documents/genes/etc.) based on the
model. In some cases (e.g. Naive Bayes, kNN), the "learning" is virtually
non-existent and simply involves transforming the training data into a form
that makes prediction easy/efficient. In other cases (e.g. SVM, ordinal
regression, NN, non-naive Bayesian Network), learning involves non-trivial
optimization, often requiring much more memory & computation than that of
prediction, and there is value in being able to "save" a model for use
elsewhere.
The format is, of course, algorithm specific, so it's probably best to
consider writing a PMML on an algorithm-by-algorithm basis...
Jason
