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Table 6: Percentage of F1 error reduction in segmentation obtained by joint inference.

in
by unknown authors
"... In PAGE 6: ... Joint inference consistently outperforms isolated infer- ence. Table6 shows error reduction from isolated inference by the better-performing joint inference method (Jnt-Seg in CiteSeer, Jnt-Seg-ER in Cora). Improvement among poten- tial citations is substantial: in Cora, error is reduced by half in total, 67% for authors, and 56% for titles; in CiteSeer, er- ror is reduced by 31% in total, 35% for authors, and 44% for titles.... ..."

Table 4: Comparison of the performance of the indepen- dent and joint inference models on the verb sense and SCF tasks,evaluated on the Senseval-2 test set, for each of the 29 verbs in the study. These results were obtained with no per-verb parameter optimization. Note the great variation in problem difficulty and joint model perfor- mance across verbs.

in Verb Sense and Subcategorization: Using Joint Inference to Improve Performance on Complementary Tasks
by Galen Andrew, Trond Grenager, Christopher Manning 2004
Cited by 1

Table 4: Comparison of the performance of the indepen- dent and joint inference models on the verb sense and SCF tasks,evaluated on the Senseval-2 test set, for each of the 29 verbs in the study. These results were obtained with no per-verb parameter optimization. Note the great variation in problem difficulty and joint model perfor- mance across verbs.

in Verb Sense and Subcategorization: Using Joint Inference to Improve Performance on Complementary Tasks
by Galen Andrew, Trond Grenager, Christopher Manning 2004
Cited by 1

Table 4: Comparison of the performance of the indepen- dent and joint inference models on the verb sense and SCF tasks,evaluated on the Senseval-2 test set, for each of the 29 verbs in the study. These results were obtained with no per-verb parameter optimization. Note the great variation in problem difficulty and joint model perfor- mance across verbs.

in Verb Sense and Subcategorization: Using Joint Inference to Improve Performance on Complementary Tasks
by Galen Andrew, Trond Grenager, Christopher Manning 2004
Cited by 1

Table 4: Comparison of the performance of the indepen- dent and joint inference models on the verb sense and SCF tasks,evaluated on the Senseval-2 test set, for each of the 29 verbs in the study. These results were obtained with no per-verb parameter optimization. Note the great variation in problem difficulty and joint model perfor- mance across verbs.

in Verb Sense and Subcategorization: Using Joint Inference to Improve Performance on Complementary Tasks
by Galen Andrew, Trond Grenager, Christopher Manning 2004
Cited by 1

Table 3: Number of inferred hotspots in 89 difierent genes. Results are given for both the separate and joint analysis of the two populations (EA: European- American; AA: African-American), and each method used a likelihood penalty of 18. Total gives the total number of hotspots inferred across the 89 genes.

in
by Paul Fearnhead, Nick G. C. Smith
"... In PAGE 24: ...Table3 , and details of the position of the inferred hotspots are given in Supplementary Tables 1 and 2. The results difier across the two analyses; with the joint analysis inferring more hotspots, though this is consistent with the higher power and slightly higher false pos- itive rate observed in the simulation study.... ..."

Table 1: Overall results (top) and detailed results on the WSJ test (bottom).

in Generalized inference with multiple . . .
by Peter Koomen, Vasin Punyakanok, Dan Roth, Wen-tau Yih 2005
"... In PAGE 4: ...35 Table 2: The results of individual systems and the result with joint inference on the development set. Overall results on the development and test sets are shown in Table1 . Table 2 shows the results of individual systems and the improvement gained by the joint inference on the development set.... ..."
Cited by 6

Table 4. Some Inference Results Over the IRIS Domain

in Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
by Alberto Ruiz, M. Carmen Garrido 1998
"... In PAGE 23: ...00 1 Table 3. Parameters of the Iris Data Joint Density Model Table4 shows the results of the inference process in the following illustrative situations: Case 1: Attribute z is known: S = {z = 5}. Case 2: Attributes x and U are known: S = {(x = 5.... ..."
Cited by 5

Table 4. Some Inference Results Over the IRIS Domain

in Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
by Alberto Ruiz, M. Carmen Garrido 1998
"... In PAGE 23: ...00 1 Table 3. Parameters of the Iris Data Joint Density Model Table4 shows the results of the inference process in the following illustrative situations: Case 1: Attribute z is known: S = {z = 5}. Case 2: Attributes x and U are known: S = {(x = 5.... ..."
Cited by 5

Table 4. Some Inference Results Over the IRIS Domain

in Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
by Alberto Ruiz, Pedro E. López-de-teruel, M. Carmen Garrido 1998
"... In PAGE 23: ...00 1 Table 3. Parameters of the Iris Data Joint Density Model Table4 shows the results of the inference process in the following illustrative situations: Case 1: Attribute z is known: S = {z = 5}. Case 2: Attributes x and U are known: S = {(x = 5.... ..."
Cited by 5
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