It is supervised learning technique because it learns through examples.

It constructs class definitions by various classification methods.

Inductive learning tries to find a hypothesis which approximates a set of samples for defining a target function “T”.

Simplest form: learn a function from examples

f is the target function

An example is a pair (x, f(x))

Problem: find a hypothesis h such that h ≈ f given a training set of examples

This is a highly simplified model of real learning:

• Ignores prior knowledge

• Assumes examples are given.

Construct/adjust h to agree with f on training set

(h is consistent if it agrees with f on all examples)

E.g., curve fitting:

Construct/adjust h to agree with f on training set

(h is consistent if it agrees with f on all examples)

E.g., curve fitting:

Construct/adjust h to agree with f on training set

(h is consistent if it agrees with f on all examples)

E.g., curve fitting:

Construct/adjust h to agree with f on training set

(h is consistent if it agrees with f on all examples)

E.g., curve fitting