P07-1010 margin-based algorithm with efficient kernel computation . Experimental results show that
P06-1016 sub-trees are applied in the tree kernel computation . Although tree kernel-based
N13-1093 , 2006 ; Joachims , 1999 ) for kernel computation . In Stage 2 , each candidate
E09-1071 2007 ) . The efficiency of the kernel computation is dominated by the | A | x |
N06-2025 average running time of our tree kernel computation is linear . In the remainder
E09-1066 memory occupancy as well as on the kernel computation speed . To empirically verify
E06-1015 learning time and the average kernel computation time . On the other hand , we
D09-1010 a subset of c1 . Observing the kernel computation in this way is important . Elements
E14-1023 frame parse information in the kernel computation that calculates similarity between
D13-1144 , while the procedure for the kernel computation remains the same . Finally ,
P06-2010 kernel plays the main role in PAF kernel computation , as shown in Figure 5 . Here
P07-1010 language model ( DLM ) assigns kernel computation . This enables us to employ com
N09-2060 task need be calculated , and the kernel computation for each relation subtask can
D09-1102 structured information well in the tree kernel computation . Gener - ally , the more a parse
D09-1112 subtrees taken into account by kernel computations but they also increase the robustness
E06-1015 algorithm for the subtree ( ST ) kernel computation , was designed in ( Vishwanathan
J08-2003 rooted in such nodes to make the kernel computation faster . The results show a several-hundred-fold
P07-1010 margin-based learning with fast kernel computation The DLM-PN can be trained by
E06-1015 proposed . Since QTK was used for the kernel computation , the high learning complexity
D09-1010 , in Sec . 4.5 , we report the kernel computation we compare against presented
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