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
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;
Conference_Titel :
Modern Problems of Radio Engineering, Telecommunications and Computer Science, 2002. Proceedings of the International Conference
Print_ISBN :
966-553-234-0
DOI :
10.1109/TCSET.2002.1015895
