Genetic Programming with Local Improvement for Visual
Learning from Examples
K. Krawiec. Proceedings 9th International Conference on Computer
Analysis of Images and Patterns, CAIP 2001, volume 2124 of Lecture Notes in Computer Science, page 209--216. Warsaw, Poland, Springer-Verlag, (September 2001)
Abstract
This paper investigates the use of evolutionary
programming for the search of hypothesis space in
visual learning tasks. The general goal of the project
is to elaborate human-competitive procedures for
pattern discrimination by means of learning based on
the training data (set of images). In particular, the
topic addressed here is the comparison between the
'standard' genetic programming (as defined by Koza
13) and the genetic programming extended by local
optimization of solutions, so-called genetic local
search. The hypothesis formulated in the paper is that
genetic local search provides better solutions (i.e.
classifiers with higher predictive accuracy) than the
genetic search without that extension. This supposition
was positively verified in an extensive comparative
experiment of visual learning concerning the
recognition of handwritten characters.
%0 Conference Paper
%1 Krawiec-chapter:2001
%A Krawiec, Krzysztof
%B Proceedings 9th International Conference on Computer
Analysis of Images and Patterns, CAIP 2001
%C Warsaw, Poland
%D 2001
%E Skarbek, W.
%I Springer-Verlag
%K algorithms, examples from genetic learning learning, local programming, search, visual
%P 209--216
%T Genetic Programming with Local Improvement for Visual
Learning from Examples
%U http://link.springer.de/link/service/series/0558/bibs/2124/21240209.htm
%V 2124
%X This paper investigates the use of evolutionary
programming for the search of hypothesis space in
visual learning tasks. The general goal of the project
is to elaborate human-competitive procedures for
pattern discrimination by means of learning based on
the training data (set of images). In particular, the
topic addressed here is the comparison between the
'standard' genetic programming (as defined by Koza
13) and the genetic programming extended by local
optimization of solutions, so-called genetic local
search. The hypothesis formulated in the paper is that
genetic local search provides better solutions (i.e.
classifiers with higher predictive accuracy) than the
genetic search without that extension. This supposition
was positively verified in an extensive comparative
experiment of visual learning concerning the
recognition of handwritten characters.
@inproceedings{Krawiec-chapter:2001,
abstract = {This paper investigates the use of evolutionary
programming for the search of hypothesis space in
visual learning tasks. The general goal of the project
is to elaborate human-competitive procedures for
pattern discrimination by means of learning based on
the training data (set of images). In particular, the
topic addressed here is the comparison between the
'standard' genetic programming (as defined by Koza
[13]) and the genetic programming extended by local
optimization of solutions, so-called genetic local
search. The hypothesis formulated in the paper is that
genetic local search provides better solutions (i.e.
classifiers with higher predictive accuracy) than the
genetic search without that extension. This supposition
was positively verified in an extensive comparative
experiment of visual learning concerning the
recognition of handwritten characters.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Warsaw, Poland},
author = {Krawiec, Krzysztof},
biburl = {https://www.bibsonomy.org/bibtex/2d2e819ebf3188b9347147e7ea1def9cb/brazovayeye},
booktitle = {Proceedings 9th International Conference on Computer
Analysis of Images and Patterns, CAIP 2001},
editor = {Skarbek, W.},
interhash = {af910f83f878c1c19acc26f95070e8f9},
intrahash = {d2e819ebf3188b9347147e7ea1def9cb},
issn = {0302-9743},
keywords = {algorithms, examples from genetic learning learning, local programming, search, visual},
month = {September 5-7},
notes = {A1 Institute of Computing Science, Poznan University
of Technology,Piotrowo 3A, 60965 Poznan, Poland
krawiec@cs.put.poznan.pl},
pages = {209--216},
publisher = {Springer-Verlag},
publisher_address = {Heidelberg},
series = {Lecture Notes in Computer Science},
timestamp = {2008-06-19T17:44:21.000+0200},
title = {Genetic Programming with Local Improvement for Visual
Learning from Examples},
url = {http://link.springer.de/link/service/series/0558/bibs/2124/21240209.htm},
volume = 2124,
year = 2001
}