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. Author manuscript; available in PMC: 2018 Feb 16.
Published in final edited form as: Mach Learn. 2005 Jun 8;61(1-3):71–103. doi: 10.1007/s10994-005-1123-6

Table 3.

Performance comparisons between different classification methods on 1000 simulated training spaces. The second column gives the average over all training spaces of the 11-point average precision for each of the different methods. The counts listed in a given row and column give the number of training spaces on which the performance of the method listed on that row outperformed/equaled/fell below the method listed at the top of that column.

11-pap PAV-Ada LinAda StmpAda Bayes’
Ideal 0.7721 962/38/0 993/7/0 990/10/0 993/7/0
PavAda 0.7321 949/41/10 885/37/78 954/44/2
LinAda 0.7053 10/41/949 220/534/246 67/901/32
StmpAda 0.7047 78/37/885 246/534/220 284/494/222
Bayes’ 0.7042 2/44/954 32/901/67 222/494/284
Random 0.5039 0/0/1000 0/0/1000 0/0/1000 0/0/1000