| measure(ment),6-3-P95-1027,ak | automatically collected data </term> , the <term> | accuracy | </term> and <term> applicability </term> of | #27835 Using automatically collected data, the accuracy and applicability of each method is quantified, and a statistical analysis of the significance of the results is performed. | |
| measure(ment),8-3-P95-1027,ak | </term> , the <term> accuracy </term> and <term> | applicability | </term> of each method is quantified , and | #27837 Using automatically collected data, the accuracy and applicability of each method is quantified, and a statistical analysis of the significance of the results is performed. | |
| measure(ment),21-3-P95-1027,ak | statistical analysis </term> of the <term> | significance | </term> of the results is performed . We | #27850 Using automatically collected data, the accuracy and applicability of each method is quantified, and a statistical analysis of the significance of the results is performed. | |
| other,29-4-P95-1027,ak | better than would be attributable to <term> | chance | </term> . In addition , one of the simplest | #27886 We show that some simple methods are indeed good indicators for the answer to the problem while other proposed methods fail to perform better than would be attributable to chance . | |
| measure(ment),20-6-P95-1027,ak | individual methods , and compare their <term> | performance | </term> to the simple methods . The most | #27924 We also apply two generic statistical learning methods for combining the indications of the individual methods, and compare their performance to the simple methods. | |
| measure(ment),14-7-P95-1027,ak | , but statistically significant , <term> | improvement | </term> over the original tests . <term> Large-scale | #27944 The most sophisticated complex learning method offers a small, but statistically significant, improvement over the original tests. |
