ON DEGREES OF FREEDOM IN ARTIFICIAL NEURAL NETWORKS

摘要

Three common senses of degrees of freedom found in the neural network literature are discussed.The unique ability of multivariate artificial neural network models to share parameters acrossdependent variables is demonstrated. This feature of neural network models can be advantageouswhen data is limited, when dependent variables are correlated, and/or when a hidden factor ispresent.