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A Machine Learning Application for Classification of Chemical Spectra

ARAN - Access to Research at NUI Galway

Show simple item record Madden, Michael G. en Howley, Tom en 2009-06-02T11:17:35Z en 2009-06-02T11:17:35Z en 2008 en
dc.identifier.citation A Data-Driven Exploration of Factors Affecting Student Performance in a Third-Level Institution , Michael G. Madden (NUI, Galway), William Lyons and Ita Kavanagh (Limerick Institute of Technology). Proceedings of AICS-2008: 19th Irish Conference on Artificial Intelligence and Cognitive Science, Cork, August 2008. en
dc.identifier.uri en
dc.description.abstract This paper presents a software package that allows chemists to analyze spectroscopy data using innovative machine learning (ML) techniques. The package, designed for use in conjunction with lab-based spectroscopic instruments, includes features to encourage its adoption by analytical chemists, such as having an intuitive graphical user interface with a step-by-step `wizard¿ for building new ML models, supporting standard file types and data preprocessing, and incorporating well-known standard chemometric analysis techniques as well as new ML techniques for analysis of spectra, so that users can compare their performance. The ML techniques that were developed for this application have been designed based on considerations of the defining characteristics of this problem domain, and combine high accuracy with visualization, so that users are provided with some insight into the basis for classification decisions. en
dc.format application/pdf en
dc.language.iso en en
dc.subject Chemometrics en
dc.subject Chemical spectra en
dc.subject Machine learning en
dc.subject Spectroscopy data en
dc.subject.lcsh Chemometrics en
dc.subject.lcsh Machine learning en
dc.subject.lcsh Chemical elements -- Spectra en
dc.title A Machine Learning Application for Classification of Chemical Spectra en
dc.type Conference Paper en

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