| D10-1023 | not exploit the full power of | linear kernel | SVMs . One strength of linear |
| D09-1012 | ESL , we use a non-normalized , | linear kernel | . No further parametrization |
| D08-1014 | implementation ( Fan et al. , 2005 ) with a | linear kernel | . The automatic translation of |
| C04-1002 | observed time of the parser with a | linear kernel | using the same machine . The |
| C04-1132 | ( Sch " olkopf , 2000 ) with a | linear kernel | , where the parameter n = 0.0001 |
| D09-1119 | C ) . Interestingly , for the | linear kernel | , SVM anchoring reduces to L2-SVM |
| C02-1019 | separable . Thus we will use the | linear kernel | only . As the SVMs is a binary |
| D10-1023 | kernel SVMs . One strength of | linear kernels | is that they make it easy to |
| D08-1015 | specify the type of kernel to use . | Linear kernel | is selected for both SVM and |
| D10-1023 | heuristic variants as features in a | linear kernel | SVM learner . We believe that |
| C04-1070 | Table 1 . The BoW run uses the | linear kernel | , while the BoC runs use the |
| D09-1073 | structured features and Kl is the | linear kernel | ( dot-product ) over the linear |
| D09-1012 | using all the available data and a | linear kernel | . 3.5 Explicit Space Classification |
| D09-1133 | classification , we adopt the | linear kernel | and the training parameter C |
| D08-1097 | been commonly accepted that the | linear kernel | of K ( xi , xj ) = xiTxi is good |
| D08-1013 | SVM , we use the LibSVM3 with a | linear kernel | function4 . For MaxEnt , we use |
| D09-1157 | For bag - of-word features , a | linear kernel | was used , and for syntactic |
| C04-1002 | parser , we built a parser with a | linear kernel | and ran it on the same test set |
| D08-1098 | ( Chang and Lin , 2001 ) with | linear kernel | . as ■ GE : This is a state-of-the-art |
| D08-1047 | was comparable to the SVM with | linear kernel | in this exper - iment . However |
