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SIIA Público
SISTEMA INTEGRAL DE INFORMACIÓN ACADÉMICA - PÚBLICO
Título del libro: Multivariate Approximation Methods Using Polynomial Models: A Comparative Study
A frequent problem in artificial intelligence is the one associated with
the so-called supervised learning: the need to find an expression of a
dependent variable as a function of several independent ones. There are
several algorithms that allow us to find a solution to the bivariate
problems. However, the true challenge arises when the number of
independent variables is large. Relatively new tools have been developed
to tackle this kind of problems. Thus, multi-Layer Perceptron networks
(MLPs) may be seen as multivariate approximation algorithms. However, a
commonly cited disadvantage of MLPs is that they remain a
``black-box'' kind of method: they do not yield an explicit closed
expression to the solution. Rather, we are left with the need of
expressing it via the architecture of the MLP and the value of the
trained connections. In this paper we explore three methods that allow
us to express the solution to multivariate problems in a closed form: a)
Fast Ascent (FA), b) Levenberg-Marquardt (LM) and c) Powell's Dog-Leg
(PM) algorithms. These yield closed expressions when presented with
multiple independent variable problems. In this paper we discuss and
compare these four methods and their possible application to pattern
recognition in mobile robot environments and artificial intelligence in
general.
Entidades citadas de la UNAM:
Fuente:
ISBN: 9781509003235 Editorial: Institute of Electrical and Electronics Engineers Inc.
