Automatic bias learning: an inquiry into the inductive basis of induction

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Bensusan:thesis,
  author =       "Hilan N. Bensusan",
  title =        "Automatic bias learning: an inquiry into the inductive
                 basis of induction",
  school =       "University of Sussex",
  year =         "1999",
  type =         "D. Phil.",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, CIGA",
  URL =          "http://www.cs.bris.ac.uk/Publications/Papers/1000410.pdf",
  size =         "217 pages",
  abstract =     "This thesis combines an epistemological concern about
                 induction with a computational exploration of inductive
                 mechanisms. It aims to investigate how inductive
                 performance could be improved by using induction to
                 select appropriate generalisation procedures. The
                 thesis revolves around a meta-learning system, called
                 The Entrencher, designed to investigate how inductive
                 performances could be improved by using induction to
                 select appropriate generalisation procedures. The
                 performance of The Entrencher is discussed against the
                 background of epistemological issues concerning
                 induction, such as the role of theoretical vocabularies
                 and the value of simplicity.

                 After an introduction about machine learning and
                 epistemological concerns with induction, Part I looks
                 at learning mechanisms. It reviews some concepts and
                 issues in machine learning and presents The Entrencher.
                 The system is the first attempt to develop a learning
                 system that induces over learning mechanisms through
                 algorithmic representations of tasks.

                 Part II deals with the need for theoretical terms in
                 induction. Experiments where The Entrencher selects
                 between different strategies for representation change
                 are reported. The system is compared to other methods
                 and some conclusions are drawn concerning how best to
                 use the system.

                 Part III considers the connection between simplicity
                 and inductive success. Arguments for Occam's razor are
                 considered and experiments are reported where The
                 Entrencher is used to select when, how and how much a
                 decision tree needs to be pruned.

                 Part IV looks at some philosophical consequences of the
                 picture of induction that emerges from the experiments
                 with The Entrencher and goes over the motivations for
                 meta-learning. Based on the picture of induction that
                 emerges in the thesis, a new position in the scientific
                 realism debate, transcendental surrealism, is proposed
                 and defended. The thesis closes with some
                 considerations concerning induction, justification and
                 epistemological naturalism.",
  notes =        "System in \cite{bensusan:1996:ciGP} called CIGA
                 Constructive induction with a Genetic Algorithm",
}

Genetic Programming entries for Hilan Bensusan

Citations