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dc.contributor.authorMadden, Michael G.en
dc.contributor.authorHowley, Tomen
dc.date.accessioned2009-06-02T11:17:35Zen
dc.date.available2009-06-02T11:17:35Zen
dc.date.issued2008en
dc.identifier.citationA 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.urihttp://hdl.handle.net/10379/205en
dc.description.abstractThis 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.formatapplication/pdfen
dc.language.isoenen
dc.subjectChemometricsen
dc.subjectChemical spectraen
dc.subjectMachine learningen
dc.subjectSpectroscopy dataen
dc.subject.lcshChemometricsen
dc.subject.lcshMachine learningen
dc.subject.lcshChemical elements -- Spectraen
dc.titleA Machine Learning Application for Classification of Chemical Spectraen
dc.typeConference Paperen
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