CS 2750 Machine
Learning

Weak learnability=Strong (PAC)
learnability

•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)?