Thesis (MSc) -- Stellenbosch University , 2003. / ENGLISH ABSTRACT: The integration of symbolic knowledge with artificial neural networks is becoming an
increasingly popular paradigm for solving real-world problems. This paradigm named
knowledge-based neurocomputing, provides means for using prior knowledge to determine
the network architecture, to program a subset of weights to induce a learning bias
which guides network training, and to extract refined knowledge from trained neural
networks. The role of neural networks then becomes that of knowledge refinement. It
thus provides a methodology for dealing with uncertainty in the initial domain theory.
In this thesis, we address several advantages of this paradigm and propose a solution
for the open question of determining the strength of this learning, or inductive, bias.
We develop a heuristic for determining the strength of the inductive bias that takes the
network architecture, the prior knowledge, the learning method, and the training data
into consideration.
We apply this heuristic to well-known synthetic problems as well as published difficult
real-world problems in the domain of molecular biology and medical diagnoses. We
found that, not only do the networks trained with this adaptive inductive bias show
superior performance over networks trained with the standard method of determining
the strength of the inductive bias, but that the extracted refined knowledge from these
trained networks deliver more concise and accurate domain theories. / AFRIKAANSE OPSOMMING: Die integrasie van simboliese kennis met kunsmatige neurale netwerke word 'n toenemende
gewilde paradigma om reelewereldse probleme op te los. Hierdie paradigma
genoem, kennis-gebaseerde neurokomputasie, verskaf die vermoe om vooraf kennis te
gebruik om die netwerkargitektuur te bepaal, om a subversameling van gewigte te
programeer om 'n leersydigheid te induseer wat netwerkopleiding lei, en om verfynde
kennis van geleerde netwerke te kan ontsluit. Die rol van neurale netwerke word dan die
van kennisverfyning. Dit verskaf dus 'n metodologie vir die behandeling van onsekerheid
in die aanvangsdomeinteorie.
In hierdie tesis adresseer ons verskeie voordele wat bevat is in hierdie paradigma en stel
ons 'n oplossing voor vir die oop vraag om die gewig van hierdie leer-, of induktiewe
sydigheid te bepaal. Ons ontwikkel 'n heuristiek vir die bepaling van die induktiewe
sydigheid wat die netwerkargitektuur, die aanvangskennis, die leermetode, en die data
vir die leer proses in ag neem.
Ons pas hierdie heuristiek toe op bekende sintetiese probleme so weI as op gepubliseerde
moeilike reelewereldse probleme in die gebied van molekulere biologie en mediese diagnostiek.
Ons bevind dat, nie alleenlik vertoon die netwerke wat geleer is met die
adaptiewe induktiewe sydigheid superieure verrigting bo die netwerke wat geleer is met
die standaardmetode om die gewig van die induktiewe sydigheid te bepaal nie, maar
ook dat die verfynde kennis wat ontsluit is uit hierdie geleerde netwerke meer bondige
en akkurate domeinteorie lewer.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/53463 |
| Date | 04 1900 |
| Creators | Snyders, Sean |
| Contributors | Omlin, Christian W., Stellenbosch University. Faculty of Science. Dept. of Mathematical Scineces. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 89 p. : ill. |
| Rights | Stellenbosch University |
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