Multivariate Classification for Qualitative Analysis

Infrared Spectroscopy for Food Quality Analysis and Control

Volumn , Issue , 2008, Pages 83-104

  Ballabio, Davide ^a   Todeschini, Roberto ^a  

^a UNIVERSITY OF MILANO BICOCCA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84896360826     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-0-12-374136-3.00004-3     Document Type: Chapter

References (26)

1. M Barker and WS Rayens (2003) Partial least squares for discrimination. Journal of Chemometrics 17 166-173.
2. LJ Breiman, JH Friedman, R Olsen and C Stone (1984) Classification and Regression Trees. Belmont, CA: Wadsworth International Group, Inc
3. CJC Burges (1998) A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2 121-167.
4. TM Cover and PE Hart (1967) Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13 21-27.
5. MP Derde and DL Massart (1986) UNEQ: a disjoint modelling technique for pattern recognition based on normal distribution. Analytica Chimica Acta 184 33-51.
6. RA Fisher (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics 7 179-188.
7. IE Frank and R Todeschini (1994) The Data Analysis Handbook. Amsterdam: Elsevier
8. JH Friedman (1989) Regularized discriminant analysis. Journal of the American Statistical Association 84 165-175.
9. DJ Hand (1997) Construction and Assessment of Classification Rules. Chichester: Wiley
10. R Hecht-Nielsen (1987) Counter-propagation networks. Applied Optics 26 4979-4984.
11. RJ Jennrich (1977) Stepwise discriminant analysis. K Enslein, A Ralston, HF Wilf (Eds) Statistical Methods for Digital Computers NewYork: Wiley & Sons
12. IT Jolliffe (1986) Principal Component Analysis. New York: Springer-Verlag
13. T Kohonen (1988) Self-Organization and Associative Memory. Berlin: Springer Verlag
14. BR Kowalski and CF Bender (1972) The K-nearest neighbor classification rule (pattern recognition) applied to nuclear magnetic resonance spectral interpretation. Analytical Chemistry 44 1405-1411.
15. KV Mardia, JT Kent and JM Bibby (1979) Multivariate Analysis. London: Academic Press
16. DL Massart and L Kaufman (1983) The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis. New York: Wiley
17. G McLachlan (1992) Discriminant Analysis and Statistical Pattern Recognition. New York: Wiley
18. L Nørgaard, R Bro, F Westad and SB Engelsen (2007) A modification of canonical variates analysis to handle highly collinear multivariate data. Journal of Chemometrics 20 425-435.
19. R Todeschini (1989) K-nearest neighbour method: the influence of data transformations and metrics. Chemometrics and Intelligent Laboratory Systems 6 213-220.
20. R Todeschini, D Ballabio, V Consonni, A Mauri and M Pavan (2007) CAIMAN (classification and influence matrix analysis): a new approach to the classification based on leverage-scaled functions. Chemometrics and Intelligent Laboratory Systems 87 3-17.
21. V Vapnik (1999) The Nature of Statistical Learning Theory. New York: Springer-Verlag
22. H Wold (1966) Estimation of principal components and related models by iterative least squares. PR Krishnaiah (Eds) Multivariate Analysis New York: Academic Press
23. S Wold (1976) Pattern recognition by means of disjoint principal components models. Pattern Recognition 8 127-139.
24. J Zupan (1994) Introduction to artificial neural network (ANN) methods: What they are and how to use them. Acta Chimica Slovenica 41 327-352.
25. J Zupan and J Gasteiger (1993) Neural Networks for Chemists: An Introduction. Weinheim: VCH-Verlag
26. J Zupan, M Novic and I Ruisánchez (1997) Kohonen and counterpropagation artificial neural networks in analytical chemistry. Chemometrics and Intelligent Laboratory Systems 38 1-23.