THE DISCRIMINANT ANALYSIS APPLIED TO THE DIFFERENTIATION OF SOIL TYPES
DOI:
https://doi.org/10.5937/ekoPolj1704513DKeywords:
analysis, differentiation, soil, types, plant.Abstract
It is frequently important in agroeconomics, on examing form example in plant breeding the problem might be to decide whether a plant or plant progeny belons to a high-yealding or low-yealding grop up. Sometimes decisions can be made on the basic of a single varialble, but more often of the 2 group differ in several variables, each of which gives some indication as to group in which the individual should be placed. This is a clasical problem of discrimination, where the general problem is to fnd a disrimination function.
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2. Breiman, L., Friedman, J.H., Olshen, R.A., Stone C.J. (1984): Classifcation and Regression Trees, Wadsworth, Belmont.
3. Chercassky, V., Mučier, F.M. (2007): Learning from Data: Concept, Theory and Mehods, 2ed, Jogn Wiley –IEEE Press.
4. Farlov, S. (1984): Self-Organizing methods in Modeling: GMDH tуре Algorithm, Taylor and Francis.
5. Forsyth, R.(1989): Machine Learning: Princples and technics, London: Chapman and Hall.
6. Gilad-Bachrach, N. F.(2006): Large margin principles for feature selection", In Guvon, G., Sikravesh, Z.(2006): Feature extraction, foundations and applications, SpringerVerlag.
7. Gilad-Bachrach, N. F.(2004): Margin based feature selection - theory and algorithms, InProc. 21st ICML.
8. Han, J., & Camber, M. (2000): Data mining concepts and techniques, San Diego, USA: Morgan Kaufman.
9. Hart, A. (1989): Machine induction as a form of knowledge acquisition in knowledge engineering, in Forsyth. R. (1989): Machine Learning: Principles and techniques, Chapman and Hall, London.
10.Haussler, D. (1990): Probably approximately correct learning. In Proc. of the 8 th National Conference on Artifcial Intelligence, pp. 1101-1108, Morgan Kaufmann.
11. Kantardzic, M. (2011): Data mining: concepts, models, methods, and algorithms. WilleyIEEE Press.
12.Kardaun, O.J.W.F., Itoh, S.I., Itoh, K., and Kardaun, J.W.P.F. (1993) Discriminant Analysis to Predict the Occurrence of ELMS in H-Mode Discharges, Nagoya, Japan: National Institute for Fusion Science.
13.Kohavi, R. (1995): A Study of Cross-validation and Bootstrap for Accuracy Estimation and Model Selection, Proc. of International Joint Conference on Artifcial Intelligence.
14.Koteri, S., Lester, R. (2012): The Role of Accounting in the Financial Crisis: Lessons For The Future, Accounting Horizons, vol. 26. No.2, pp. 335-352.
15.Milojević, I., Vukoje, A., Mihajlović, M. (2013): Accounting consolidation of the balance by the acquisition method, Ekonomika poljoprivrede, vol. 60, no. 2 , pp. 237-252, Društvo agrarnih ekonomista, Beograd, Srbija.
16.Quinlan, R. J., Cameron-Jonas, R.R. (1995): Introduction of Logic Programs: FOIL and Related Systems, New Generation Computing, vol 13, pp.287-312.
17.Stanojević, S., Đorđević, N., Volf, D. (2017): Primena kvantitativnih metoda u privređivanju poslovanja privrednih društava , ODITOR, vol. 3, No. 1, pp. 91-101. Centar za ekonomska i fnansijska istraživanja, Beograd, Srbija.
18.Thanh, T.D., Moncef, G., & Alexandros, I. (2017): Multilinear class-specifc discriminant analysis, Aalborg, Pattern Recognition Letters, vol. 93, No. 3, pp. 131-136, Elsevier, Denmark.
19.Vukoje, A. (2013): Faktori egzistencije kao uslov stvaranja tržišne pozicije preduzeća, ODITOR, vol. 1, No. 5, pp. 27-37, Centar za ekonomska i fnansijska istraživanja, Beograd, Srbija.
20.Written, I.H., Frank, E.(2005): Data Mining: Practical machine learning tools and techniques, 2 end edition, Morgan Kaufman, San Francisco.
21.Zhijin, J., Taijie, W., Yigang, X. & Tao, H. (1994): The use of the discriminant analysis method for e π μ separation in BES, Netherlands: Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 345, No. 3, pp. 541-548.