11-statlearntheory - 1 Foundations of Artificial...

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Unformatted text preview: 1 Foundations of Artificial Intelligence Statistical Learning Theory CS472 Fall 2007 Thorsten Joachims Outline Questions in Statistical Learning Theory: How good is the learned rule after n examples? How many examples do I need before the learned rule is accurate? What can be learned and what cannot? Is there a universally best learning algorithm? In particular, we will address: What is the true error of h if we only know the training error of h? Finite hypothesis spaces and zero training error (Finite hypothesis spaces and non-zero training error) Game: Randomized 20-Questions Game: 20-Questions I think of object f For i = 1 to 20 You get to ask 20 yes/no questions about f and I have to answer truthfully You make a guess h You win, if f=h Game: Randomized 20-Questions I pick function f H , where f : X {-1,+1} For i = 1 to 20 World delivers instances x X with probability P(x) and I have to tell you f(x) You form hypothesis h H trying to guess my f H You win if f(x)=h(x) with probability at least 1- for x drawn according to P(x)....
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This note was uploaded on 02/19/2008 for the course CS 4700 taught by Professor Joachims during the Fall '07 term at Cornell University (Engineering School).

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11-statlearntheory - 1 Foundations of Artificial...

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