PAC-Learnable

A concept C is PAC-Learnable by a hypothesis space H if:

There exists an algorithm A that terminates in time polynomial in the number of training examples
There are a polynomial number of examples given δ and ε
A terminates with probablity 1 - δ a learned function f such that Error(f) ≤ ε

Calculating Example Sizes

import math

def calc_m(epsilon, delta, f, v):
    H = f**v
    return math.log(delta / H) / math.log(1 - epsilon)