(Mr. Tetlock's work, as I mentioned, found that statistically driven approaches performed well as compared with expert judgment).
However, both approaches performed equally well in cross-validation.
All the transfer learning approaches performed better than the baseline approach.
The phenomenon was previously described by experimental approaches performed in the laboratory.
The minimal and seasonal approaches performed significantly less accurately in both of the study areas.
In Figure 11, we can observe that both approaches performed with median rankings of 4 (GOOD) and 5 (EXCELENT).
This section highlights the use of fuzzy approaches performed particularly in the processing stage on medical images.
The metatags + passage and metatags + description + passage approaches performed similarly although the metatags + passage approach presented higher precision values at recall levels up to 60%%.
Numerical results show how both approaches performed successfully in reducing the overall system solution time cost and iterations required for convergence.
The statistical approaches performed slightly better than random choice that in turn performed slightly better than sets derived by expert knowledge.
Most metadata-free methods performed poorly for the pink cluster (challenges 152, 202, 178); all approaches performed relatively poorly for the cyan cluster adjacent (challenges 131, 126, 207 and 119).
