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Mathematical Geology

, Volume 39, Issue 3, pp 307–319 | Cite as

Support Vector Machines for Classification of Aggregates by Means of IR-Spectra

Article

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.

Keywords

Wavelets Principal component analysis Partial least squares Directed acyclic graph 

Abbreviations

DAG

Directed acyclic graph

DAGSVM

Support vector machines for multi-groups classification using DAG

MIR

Mid infrared light

NIR

Near infrared light

ORIG

Data without any transformation

ORIGPCA

Data after PCA

ORIGPLS

Data after PLS

PC

Principal component

PCA

Principal component analysis

PLS

Partial least squares

SVM

Support vector machines

WPCA

Wavelet transformed data with subsequent PCA

WPLS

Wavelet transformed data with subsequent PLS

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Copyright information

© International Association for Mathematical Geology 2007

Authors and Affiliations

  • Vera Hofer
    • 1
  • Juergen Pilz
    • 2
  • Thorgeir S. Helgason
    • 3
  1. 1.Department of Statistics and Operations ResearchKarl-Franzens UniversityGrazAustria
  2. 2.Department of MathematicsUniversity of KlagenfurtKlagenfurtAustria
  3. 3.Petromodel ehfReykjavikIceland

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