Table 1.

Learning principles

		prior knowledge
		non Bayesian	Bayesian
	generative	ML	MAP
objective	hybrid	GDT	PGDT
	discriminative	MCL	MSP

The table shows six established learning principles that can be grouped by their objective as being generative, hybrid, or discriminative and utilization of prior knowledge with the two possibilities non Bayesian and Bayesian. The four elementary learning principles are the generative, non Bayesian maximum likelihood (ML) learning principle, the generative, Bayesian maximum a posteriori (MAP) learning principle, the discriminative, non Bayesian maximum conditional likelihood (MCL) learning principle, and the discriminative, Bayesian maximum supervised posterior (MSP) learning principle. The hybrid learning principles which interpolate between generative and discriminative learning principles are the non Bayesian generative-discriminative trade-off (GDT) learning principle and the penalized generative-discriminative trade-off (PGDT) learning principle.