RELATIVE UNCERTAINTY LEARNING THEORY: AN ESSAY
Simone Fiori · International Journal of Neural Systems · 2004
The aim of this manuscript is to present a detailed analysis of the algebraic and geometric properties of relative uncertainty theory (RUT) applied to neural networks learning. Through the algebraic analysis of the original learning criterion, it is shown that RUT gives rise to principal-subspace-analysis-type learning equations. Through an algebraic-geometric analysis, the behavior of such matrix-type learning equations is illustrated, with particular emphasis to the existence of certain invariant manifolds.