Support Vector Machines for Classification of?Aggregates by Means of IR-Spectra |
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Authors: | Vera Hofer Juergen Pilz and Thorgeir S Helgason |
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Institution: | (1) Department of Statistics and Operations Research, Karl-Franzens University, Universit?tsstra?e 15/E3, 8010 Graz, Austria;(2) Department of Mathematics, University of Klagenfurt, Universit?tsstra?e 65-67, 9020 Klagenfurt, Austria;(3) Petromodel ehf, Borgartun 20, 105 Reykjavik, Iceland |
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Abstract: | The increasing physical and technical demands placed on construction materials, especially as they are being used more and
more up to the limits of their mechanical strength, has led the aggregates industry to search for more efficient methods of
quality control. Information from theoretical work on rock spectra in near-infrared and mid-infrared light as well as achievements
gained in signal processing can all be used to improve quality control in an economically acceptable manner. As engineering
properties of aggregates are to a great extent determined by the petrological composition of the rock aggregates, the question
is, whether a statistical classification rule for identification of rock aggregates can be developed. However, the classification
of rocks is complicated by the fact that the optical behavior of minerals forming the rock often appears muted. In addition,
minor constituents may dominate the spectrum. Furthermore, the relevant spectra form high dimensional data that are extremely
difficult to analyze statistically, especially when curves are very similar. In this paper, support vector machines for classification
of rock spectra are investigated, since they are appropriate in classifying highly dimensional data such as spectra. |
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Keywords: | Wavelets Principal component analysis Partial least squares Directed acyclic graph |
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