DocumentCode
1918643
Title
Selecting classifiers techniques for outcome prediction for kvazistationarity process
Author
Lesna, Natalya ; Shatovska, Tetyana ; Repka, Victoria
Author_Institution
Comput. Sci. Fac., Kharkiv Nat. Univ. of Radioelectron., Ukraine
fYear
2002
fDate
2002
Firstpage
145
Abstract
This paper presents an analysis of different techniques designed to aid a researcher in determining which of the classification techniques would be most appropriate to choose the ridge, robust and linear regression methods for predicting outcomes for specific kvazistationarity processes.
Keywords
estimation theory; prediction theory; statistical analysis; classification techniques; classifier selection; estimation method; kvazistationarity process; learning algorithm; linear regression; mathematical models; neural network; outcome prediction; ridge regression; robust regression; Architecture; Buildings; Classification tree analysis; Decision trees; Linear regression; Mathematical model; Nearest neighbor searches; Neural networks; Predictive models; Robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Modern Problems of Radio Engineering, Telecommunications and Computer Science, 2002. Proceedings of the International Conference
Print_ISBN
966-553-234-0
Type
conf
DOI
10.1109/TCSET.2002.1015895
Filename
1015895
Link To Document