Incremental Support Vector Machine Combined with Ultraviolet-Visible Spectroscopy for Rapid Discriminant Analysis of Red Wine
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[1] S. Azcarate,et al. Classification of Argentinean Sauvignon blanc wines by UV spectroscopy and chemometric methods. , 2013, Journal of food science.
[2] AbdiHervé,et al. Principal Component Analysis , 2010, Essentials of Pattern Recognition.
[3] Gu Bin. Analysis for Incremental and Decremental Standard Support Vector Machine , 2013 .
[4] W. Marsden. I and J , 2012 .
[5] Gianluca Calcagni,et al. The geometry of learning , 2016, Journal of Mathematical Psychology.
[6] Ricard Boqué,et al. Classification of edible oils and modeling of their physico-chemical properties by chemometric methods using mid-IR spectroscopy. , 2013, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.
[7] Bin Gu,et al. Analysis for Incremental and Decremental Standard Support Vector Machine: Analysis for Incremental and Decremental Standard Support Vector Machine , 2014 .
[8] Xiu-Jun Zhou,et al. [Fast discrimination of edible vegetable oil based on Raman spectroscopy]. , 2012, Guang pu xue yu guang pu fen xi = Guang pu.
[9] Ivor W. Tsang,et al. Core Vector Machines: Fast SVM Training on Very Large Data Sets , 2005, J. Mach. Learn. Res..
[10] S. Lanteri,et al. Detection of minced beef adulteration with turkey meat by UV-vis, NIR and MIR spectroscopy , 2013 .
[11] M. Vázquez,et al. Application of artificial neural networks coupled to UV–VIS–NIR spectroscopy for the rapid quantification of wine compounds in aqueous mixtures , 2015 .
[12] M. Vázquez,et al. Classification of red wines from controlled designation of origin by ultraviolet-visible and near-infrared spectral analysis , 2014 .
[13] Mark O. Downey,et al. Rapid Determination of Phenolic Components in Red Wines from UV-Visible Spectra and the Method of Partial Least Squares , 2007, American Journal of Enology and Viticulture.
[14] M. Vázquez,et al. Determination of polyphenolic compounds of red wines by UV-VIS-NIR spectroscopy and chemometrics tools. , 2014, Food chemistry.
[15] Su-Yun Huang,et al. Reduced Support Vector Machines: A Statistical Theory , 2007, IEEE Transactions on Neural Networks.
[16] David M. J. Tax,et al. Online SVM learning: from classification to data description and back , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).
[17] H. Abdi,et al. Principal component analysis , 2010 .
[18] Daniel Cozzolino,et al. The role of visible and infrared spectroscopy combined with chemometrics to measure phenolic compounds in grape and wine samples. , 2015, Molecules.
[19] F. J. Acevedo,et al. Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines. , 2007, Journal of agricultural and food chemistry.
[20] Klaus-Robert Müller,et al. Incremental Support Vector Learning: Analysis, Implementation and Applications , 2006, J. Mach. Learn. Res..