•Assume there exists a weak learner
–it is better that a random guess
(50 %) with confidence higher than 50 % on any data
distribution
•Question:
–Is problem also
PAC-learnable?
–Can we generate an algorithm P
that achieves an arbitrary (e-d) accuracy?
•Why is important?
–Usual classification methods
(decision trees, neural nets), have specified, but
uncontrollable performances.
–Can we improve performance to
achieve pre-specified accuracy (confidence)?