Title :
On the accuracy of a bootstrap estimate of the classification error
Author_Institution :
Inst. of Math. & Cybern., Acad. of Sci., Vilnius, Lithuanian SSR, USSR
Abstract :
Analytical and simulation studies show that a variance of the bootstrap estimator discussed is lower than that of the commonly used leave-one-out estimator only when sample size is extremely small or when the classification error is large. An essential feature of the bootstrap method is that observations of the training sample (TS) play the role of a general population and are used to determine the optimistic bias of the resubstitution estimate. A bootstrap training sample (BTS) is formed from the TS in a random way. A classification rule is designed using a BTS and is tested twice
Keywords :
pattern recognition; statistics; bootstrap estimate; classification error; optimistic bias; pattern recognition; resubstitution estimate; training sample; variance; Bonding; Character generation; Pattern recognition; Testing;
Conference_Titel :
Pattern Recognition, 1988., 9th International Conference on
Conference_Location :
Rome
Print_ISBN :
0-8186-0878-1
DOI :
10.1109/ICPR.1988.28478