EM in a Nutshell
Henry Kautz – CSE
473 – Intro to AI
Nov 24, 2003
Consider learning a naïve Bayes classifier using only unlabeled data:
Initialization: randomly assign numbers to P(C), P(A|C), P(B|C).
E-step: Compute P(C|A,B):
M-step: Re-compute maximum likelihood estimation of P(C), P(A|C), P(B|C):
Calculate log likelihood of data:
If still improving (increasing): repeat at E-step.
Otherwise, stop.