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Table 1. The performance achieved for different tasks for MUC-3 through MUC-7. Missing value means that the task was not performed for this competition.

in A Light-weight Approach to Coreference Resolution for Named Entities in Text (MSc. Thesis)
by Marin Dimitrov 2002
Cited by 20

Table 6. Participation in the detection task. Bullets indicate participation in the competition for a particular test set and object class.

in The 2005 pascal visual object classes challenge
by Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frederic Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-taylor, Amos Storkey, Or Szedmak, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang 2006
"... In PAGE 14: ... No. Task Training data Test data 5 Detection train+val test1 6 Detection train+val test2 7 Detection not VOC test1 or test2 test1 8 Detection not VOC test1 or test2 test2 Table6 lists the participation in competitions 5 and 6, which used the pro- vided train+val image set for training. Five of the twelve participants entered results for these competitions.... ..."
Cited by 34

Table 6. Participation in the detection task. Bullets indicate participation in the competition for a particular test set and object class.

in The 2005 pascal visual object classes challenge
by Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frederic Jurie, Thomas Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-taylor, Amos Storkey, Or Szedmak, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang 2006
"... In PAGE 14: ... No. Task Training data Test data 5 Detection train+val test1 6 Detection train+val test2 7 Detection not VOC test1 or test2 test1 8 Detection not VOC test1 or test2 test2 Table6 lists the participation in competitions 5 and 6, which used the pro- vided train+val image set for training. Five of the twelve participants entered results for these competitions.... ..."
Cited by 34

Table 5. Competitions for the detection task, defined by the choice of training data and test data.

in The 2005 pascal visual object classes challenge
by Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frederic Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-taylor, Amos Storkey, Or Szedmak, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang 2006
Cited by 34

Table 5. Competitions for the detection task, defined by the choice of training data and test data.

in The 2005 pascal visual object classes challenge
by Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frederic Jurie, Thomas Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-taylor, Amos Storkey, Or Szedmak, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang 2006
Cited by 34

Table 4.6: F-measures on the EFE newswire articles (Spanish) reported by top 3 systems participating in the CoNLL 2002 Shared Task NER competition.

in Predicting Accuracy of Extracting Information from Unstructured Text Collections
by Eugene Agichtein, Silviu Cucerzan

Table 4 Comparison of alignment error rate percentages for various training schemes (Verbmobil task; Dice+C: Dice coefficient with competitive linking).

in A Systematic Comparison of Various Statistical Alignment Models
by Franz Josef Och, Hermann Ney 2003
Cited by 271

Table 5 Comparison of alignment error rate percentages for various training schemes (Hansards task; Dice+C: Dice coefficient with competitive linking).

in A Systematic Comparison of Various Statistical Alignment Models
by Franz Josef Och, Hermann Ney 2003
Cited by 271

Table 2: Frequency of Competitive Offers

in The influence of task contexts on the decision-making of humans and computers
by Barbara Grosz, Avi Pfeffer, Stuart Shieber, Alex Allain 2007
"... In PAGE 6: ... To test this hypothesis, we performed a within-round com- parison of the offer benefit in both conditions. Table2 presents the number of rounds in which the difference between the proposed benefit for proposers and responders was positive (column Proposer gt; Responder ) and the number of rounds in which this dif- ference was negative (column Proposer lt; Responder ). As shown by the table, table proposers made offers that benefited themselves over responders significantly more of- ten than task proposers (chi-square p lt; 0.... In PAGE 6: ...en than task proposers (chi-square p lt; 0.05). These results confirm that table proposers are more likely to be competitive than proposers. Table2 also shows that 62% of all offers made by table proposers benefited them- selves more than table responders, while 60% of all offers made by task proposers ben- efited task responders more than themselves (chi-square p lt; 0.05).... ..."
Cited by 2

Table 2: Frequency of Competitive Offers

in The influence of task contexts on the decision-making of humans and computers
by Barbara Grosz, Avi Pfeffer, Stuart Shieber, Alex Allain 2007
"... In PAGE 6: ... To test this hypothesis, we performed a within-round com- parison of the offer benefit in both conditions. Table2 presents the number of rounds in which the difference between the proposed benefit for proposers and responders was positive (column Proposer gt; Responder ) and the number of rounds in which this dif- ference was negative (column Proposer lt; Responder ). As shown by the table, table proposers made offers that benefited themselves over responders significantly more of- ten than task proposers (chi-square p lt; 0.... In PAGE 6: ...en than task proposers (chi-square p lt; 0.05). These results confirm that table proposers are more likely to be competitive than proposers. Table2 also shows that 62% of all offers made by table proposers benefited them- selves more than table responders, while 60% of all offers made by task proposers ben- efited task responders more than themselves (chi-square p lt; 0.05).... ..."
Cited by 2
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