# Machine Learning

I have been working on some self study "machine learning". Based on a few posts here, I wanted to make a program that "learned" via Bayes Law. I test it with some simple truth tables. It recalls the past training data well. I note that some machines are able to make inferances in new situations, to different degrees. My particular version cannot do so, which raises a question. Perhaps it doesn't learn, perhaps it only regurgitates.

My question is: In a broader, philosophical sense, does a program (any program) still qualify as "learning" if it cannot infer about things that it has not seen historically? What are the bounds on such things?

• If you do not provide us your code we cannot comment on it - it may be just a bug in the code that makes it not work as you'd like. We don't even know what your algorithm is! The fact that your algorithm does not work on your data does not mean that in general such algorithms do not work. – Tim Mar 21 '15 at 7:43
• That's not the question, I accept that such algorithms work. The code above works perfectly as I would like. The question is: Can something (my code for instance) be said to learn if it cannot reason beyond past experiences? What's the definition on learning? – RegressForward Mar 21 '15 at 12:40
• Either case thank you for the question, You've allowed me to make a considerable improvement in clarity. – RegressForward Mar 21 '15 at 13:07

P.S. A note from the "terminology police" :-): in R , bayes() is not a command - it's a function.