Well - my endeavours are somehow marginal, because machine learning today is almost nothing more than applied statistics - it evaluates the parameters of the statistical models. By I am trying other kind of machine learning - symbolic, logical machine learning that learns symbolic knowledge. *Actually at present I don't know any good reference, resource about symbolic machine learning - so - if anyone can provide it, mention it, then I would be really happy. *
The other field that lacks development, is what I can call "*structural optimization*". E.g. one can imaginge complex business process/queue model that have service times, waiting times, availability ratios and so on. One can numerically optimize this model. But - we can imagine the structural transformation of this model in completely different business process. So - how to select the best model from the different structures - now just simlate those models and choose the best structure, but to do guided search (like gradient search in numerical optimization) towards the best structure/structural model? So - *if there are references in this field, then I would be more than happy to hear about them as well!* -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/4259343f-87c1-486d-81a1-5f8b24ae3263%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
