Updating a discriminant function on the basis of unclassified data
作者:
G. J. McLachlan,
S. Ganesalingam,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1982)
卷期:
Volume 11,
issue 6
页码: 753-767
ISSN:0361-0918
年代: 1982
DOI:10.1080/03610918208812293
出版商: Marcel Dekker, Inc.
关键词: sample discriminant function;unclassified observations;updating;asymptotic error rates;simulation experiments
数据来源: Taylor
摘要:
The problem of updating a discriminant function on the basis of data of unknown origin is studied. There are observations of known origin from each of the underlying populations, and subsequently there is available a limited number of unclassified observations assumed to have been drawn from a mixture of the underlying populations. A sample discriminant function can be formed initially from the classified data. The question of whether the subsequent updating of this discriminant function on the basis of the unclassified data produces a reduction in the error rate of sufficient magnitude to warrant the computational effort is considered by carrying out a series of Monte Carlo experiments. The simulation results are contrasted with available asymptotic results.
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