| W13-1902 | demonstrated the positive impact of | statistical feature selection | on the overall classification |
| P04-1016 | describe a method for embedding | statistical feature selection | into kernel calculation . 4.1 |
| P04-1016 | Method Our approach is based on | statistical feature selection | in contrast to the conventional |
| P04-1016 | calculate kernels efficiently with a | statistical feature selection | method . First , we briefly explain |
| P04-1016 | section shows how to integrate | statistical feature selection | in the kernel calculation . Our |
| P04-1016 | Conclusion This paper proposed a | statistical feature selection | method for convolution kernels |
| W06-2504 | for dimensionality reduction and | statistical feature selection | . In general , the anticipated |
| P04-1016 | information , we use x2 values as | statistical feature selection | criteria . Although we selected |
| C02-1103 | set as shown in statistics for | statistical feature selection | ( Yang et al. , 1997 ) . To evaluate |
| W13-1902 | In previous work , we applied | statistical feature selection | to the problem of pneumonia detection |
| P04-1057 | redundancy , perhaps by considering | statistical feature selection | methods . The definition used |
| W13-1902 | assertion analysis , and applied | statistical feature selection | . We used 10-fold cross validation |
| W15-2601 | to use medical ontologies and | statistical feature selection | to identify terms of interest |
| P04-1016 | proposes a new approach based on | statistical feature selection | that avoids this issue . To enable |
| W06-2504 | is used . In this last case , | statistical feature selection | is best dropped and a large context |
| P04-1033 | validation set . We applied a | statistical feature selection | method ( χ2 statistics ) |
| W13-1902 | assertion analysis , and ( 3 ) applied | statistical feature selection | . Our proposed methodology of |
| W06-2504 | algorithms that incorporate both | statistical feature selection | and Singular Value Decomposition |
| P04-1016 | time in parallel . As a result , | statistical feature selection | can be embedded in oroginal sequence |
| W13-1902 | represented by token n-grams , ( 2 ) | statistical feature selection | was applied to select the most |
