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< rcurtin> mikeling: let me know if I can explain any of my comments in the PR; I'm looking forward to getting it finished and merged :)
< rcurtin> I think it will be a nice improvement
< mikeling> rcurtin: thank you! I will update it within today! :)
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< rcurtin> zoq: it seems like this will fix the decision tree build on Windows: https://github.com/mlpack/mlpack/pull/858/commits/b2f9932f2831370a3546c7606c688b5756cdc95d
< rcurtin> very strange, I think it's an MSVC compiler bug, but at least there is a workaround
< zoq> curtin: What I don't get is why it worked for the sample code and failed for the decision class.
< zoq> But great that it worked without using ifdef.
< rcurtin> yeah, it's a relief to not have to do something kludgey like that
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< travis-ci> mlpack/mlpack#1825 (master - f1701b1 : Ryan Curtin): The build has errored.
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< cult-> which algorithms are useful for some kind of detection without learning on datasets, but learning from given parameters? i am not sure if it makes any sense to apply any algorithm that helps clustering without a training set, but with actual 'model' (labels for each vector) ?
< cult-> uhm. its not clear from above. so i would like to do detection. for detection there's no need for learning. but i would like to know if there is some model for detection on multi variate series that helps more than just pure detection.
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< travis-ci> mlpack/mlpack#1826 (master - a581cef : Marcus Edel): The build passed.
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< rcurtin> cult-: I'm not sure I understand the goal completely :(
< cult-> i was reading about reinforcement learning, is there any algorithm in mlpack for that?
< cult-> its not mandatory for me just studying new things.
< rcurtin> I think maybe some of the neural network code could be used for reinforcement learning tasks, but that's outside of my field of expertise so I can't say for sure how useful it might be
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