Bayesian Concept Learning
by Kevin Murphy
Consider the problem of learning to understand the meaning of a word, such as ``dog''. Presumably, as a child, one's parents point out positive examples of this concept, saying such things as, ``look at the cute dog!'', or ``mind the doggy'', etc. It is very unlikely that they provide negative examples, by saying ``look at that non-dog''.
However, most machine learning methods require negative as well as positive
examples. In this talk, I summarize work by Josh Tenenbaum that shows how one
can learn concepts from small amounts of positive only data. This provides a
very simple illustration of the power and elegance of Bayesian methods.
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