Title :
Active Methods of Knowledge Derivation based on Useful Hypotheses Generation
Author :
Khabarov, V.I. ; Zaitseva, T.S.
Abstract :
The problem of regressive experiment planning from the standpoint of knowledge derivation about the type of regressive model is considered. In the literature on experiment planning there are some articles where the hypotheses discrimination methods and methods of discriminating experiments planning are given. In the given article accent is raised as to the problem of "interesting" hypotheses shaping from the standpoint of an experimenter. It has been shown that many "interesting" hypotheses constitute a certain extremal basis in the domain of hypotheses which is considered to be the dual solution to the extremal basis incorporating the points of the experiment optimal plan.
Keywords :
design of experiments; regression analysis; experimental design; hypotheses discrimination method; hypotheses generation; knowledge derivation; regressive experiment planning problem; Combinatorial mathematics; Control systems; Cost function; Distribution functions; Fault tolerance; Frequency; Informatics; Q measurement; Systems engineering and theory; Testing;
Conference_Titel :
Modern Technique and Technologies, 2005. MTT 2005. 11th International Scientific and Practical Conference of Students, Post-graduates and Young Scientists
Conference_Location :
Tomsk
Print_ISBN :
978-0-7803-8877-2
Electronic_ISBN :
978-0-7803-8878-9
DOI :
10.1109/SPCMTT.2005.4493233