The aim of this study is to evaluate the effectiveness
of genetic programming relative to that of more
commonly-used methods for the identification of
components within mixtures of materials using Raman
spectroscopy. A key contribution of the genetic
programming technique proposed in this research is that
it explicitly aims to optimise the certainty levels
associated with discovered rules, so as to minimize the
chance of misclassification of future samples.
%0 Journal Article
%1 journals/kbs/HennessyMCR05
%A Hennessy, Kenneth
%A Madden, Michael G.
%A Conroy, Jennifer
%A Ryder, Alan G.
%D 2005
%J Knowledge Based Systems
%K Machine Neural Raman Spectroscopy, algorithms, genetic learning, networks, programming,
%N 4-5
%P 217--224
%R doi:10.1016/j.knosys.2004.10.001
%T An improved genetic programming technique for the
classification of Raman spectra
%V 18
%X The aim of this study is to evaluate the effectiveness
of genetic programming relative to that of more
commonly-used methods for the identification of
components within mixtures of materials using Raman
spectroscopy. A key contribution of the genetic
programming technique proposed in this research is that
it explicitly aims to optimise the certainty levels
associated with discovered rules, so as to minimize the
chance of misclassification of future samples.
@article{journals/kbs/HennessyMCR05,
abstract = {The aim of this study is to evaluate the effectiveness
of genetic programming relative to that of more
commonly-used methods for the identification of
components within mixtures of materials using Raman
spectroscopy. A key contribution of the genetic
programming technique proposed in this research is that
it explicitly aims to optimise the certainty levels
associated with discovered rules, so as to minimize the
chance of misclassification of future samples.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Hennessy, Kenneth and Madden, Michael G. and Conroy, Jennifer and Ryder, Alan G.},
bibdate = {2005-11-24},
bibsource = {DBLP,
http://dblp.uni-trier.de/db/journals/kbs/kbs18.html#HennessyMCR05},
biburl = {https://www.bibsonomy.org/bibtex/20b2cd926368996ea6de3dc226f5c17cb/brazovayeye},
doi = {doi:10.1016/j.knosys.2004.10.001},
interhash = {7cefa507c75d7679c7221932182f9a79},
intrahash = {0b2cd926368996ea6de3dc226f5c17cb},
journal = {Knowledge Based Systems},
keywords = {Machine Neural Raman Spectroscopy, algorithms, genetic learning, networks, programming,},
month = {August},
note = {AI-2004, Cambridge, England, 13th-15th December 2004},
number = {4-5},
pages = {217--224},
timestamp = {2008-06-19T17:41:21.000+0200},
title = {An improved genetic programming technique for the
classification of Raman spectra},
volume = 18,
year = 2005
}