On 01/09/2012 09:11 AM, Paul A. Bristow wrote:
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I, for one, think this looks a very useful library. [...] So you may need to find and drum up support from some potential (or better still, actual) users ?
I will provide a review if this library gets to review, so far, I've not looked much at this submission.
I think that machine learning is an area where there is enough maturity that implementations in Boost are desirable. A couple of things to consider. It would be desirable that the machine learning topic area be designed a bit more comprehensively than a single library. For example, I should be able to run a GA on the same data as the DE. I would hope that they have the same API. This somewhat implies to me that DE/GA are instances of training algorithms, for which, many are possible. There are many things that go into a ML solution. Data conditioning, model training, model validation, model evaluation. Training in batch modes, instance based, etc. Just to name a couple issues... Then the normal list of library desires...completely generic, optimized for every conceivable configuration (embedded processor, server farm, etc i.e. single process, multi-threaded, mpi, etc) My advice is to be prepared for the big can of worms that this topic could potentially bring up. I think the topic area will be a very hard sell, but would be very useful.