kernel.SDA: Kernel discriminant analysis for symbolic data

In symbolicDA: Analysis of Symbolic Data

Kernel discriminant analysis for symbolic data

Description

Kernel discriminant analysis for symbolic data

Usage

kernel.SDA(sdt,formula,testSet,h,...)

Arguments

sdt	symbolic data table
formula	a formula, as in the lm function
testSet	vector with numbers objects ij test set
h	kernel bandwith size
...	argumets passed to dist_SDA functon

Details

Kernel discriminant analysis for symbolic data is based on the intensity estimatior (that is based on dissimiliarity measure for symbolic data) due to the fact that classical well-known density estimator can not be applied. Density estimator can not be applied due to the fact that symbolic objects are not object of euclidean space and the integral operator for symbolic data is not applicable.

For futher details see ../doc/Kernel_SDA.pdf.pdf

Value

vector of class belongines of each object in test set

Author(s)

Andrzej Dudek [e-mail]

Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://keii.ue.wroc.pl/symbolicDA/

References

Billard L., Diday E. (eds.) (2006), Symbolic Data Analysis, Conceptual Statistics and Data Mining, John Wiley & Sons, Chichester.

Bock H.H., Diday E. (eds.) (2000), Analysis of symbolic data. Explanatory methods for extracting statistical information from complex data, Springer-Verlag, Berlin.

Diday E., Noirhomme-Fraiture M. (eds.) (2008), Symbolic Data Analysis with SODAS Software, John Wiley & Sons, Chichester.

See Also

dist_SDA

Examples

# Example 1
# LONG RUNNING - UNCOMMENT TO RUN
#sda<-parse.SO("samochody")
#model<-kernel.SDA(sda, "Typ_samochodu~.", testSet=6:16, h=0.75)
#print(model)

symbolicDA documentation built on Feb. 16, 2023, 6 p.m.