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Lecture 2: Problem Solving using State Space Representations - page 30 / 49

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Slide Set 2: State-Space Search 30

ICS 271, Fall 2007: Professor Padhraic Smyth

Learning a spam email classifier

States: settings of the parameters in our model

Initial state: random parameter settings

Actions: moving in parameter space

Goal test: optimal accuracy on the training data

Path Cost: time taken to find optimal parameters

 (Note: this is an optimization problem – many machine learning problems can be cast as optimization)

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