Concept learning: Introduction, version spaces and the candidate elimination algorithm; learning with trees: Constructing decision trees, CART, classification example. Alternatively, each concept can be thought of as a Boolean-valued function defi...
Concept learning: Introduction, version spaces and the candidate elimination algorithm; learning with trees: Constructing decision trees, CART, classification example. Alternatively, each concept can be thought of as a Boolean-valued function defined over this larger set (e.g., a function defined over all animals, whose value is true for birds and false for other animals). In this chapter we consider the problem of automatically inferring the general definition of some concept, given examples labeled as+.membersor nonmembers of the concept. This task is commonly referred to as concept learning or approx-imating a Booleanvalued function from examples.