Abstract
This paper examines a number of feature extraction algorithms to improve the classification of mineral particles when the pattern vector is defined as a series of Fourier coefficients. Considerable improvement can be obtained by using nonlinear feature extraction algorithms, and this approach provides a viable alternative to classification procedures of extremely small samples of particles based on their physical and chemical properties.
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6: References
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Bonifazi, G., Massacci, P., Patrizi, G. (1989). Alternative feature selection procedures for particle classification by pattern recognition techniques. In: Cantoni, V., Creutzburg, R., Levialdi, S., Wolf, G. (eds) Recent Issues in Pattern Analysis and Recognition. Lecture Notes in Computer Science, vol 399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-51815-0_68
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DOI: https://doi.org/10.1007/3-540-51815-0_68
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