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I have heard of the concepts of learning by analogy (which is quite self-explanatory), inductive learning and explanation-based learning. I tried to learn about inductive learning and explanation-based learning, but I don't understand them.

How would you explain all these three concepts? What are the differences between them?

A link to some explanatory article/notes/blog post are appreciated too.

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    $\begingroup$ Classical machine learning seem to fall into inductive learning. While explanation learning looks like a mixture of the process used in NLP with absurly large data augmentation $\endgroup$ – Pedro Henrique Monforte Mar 28 '19 at 15:42

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