Maximum A Posteriori Learning
	• This trades-off the prior probabilities of the parameters against the probability of the data given the parameters. It looks for the parameters that have the greatest product of the prior term and the likelihood term. • Minimizing the squared weights is equivalent to maximizing the log probability of the weights under a zero-mean Gaussian prior.
				p(w)
			w
							0