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Table 1: Characterization of Data Fusion Levels

in Abstract
by Kenneth Baclawski, Christopher J. Matheus, Mieczyslaw K. Kokar, Marek Malczewski, Jerzy Letkowski
"... In PAGE 3: ... The terminology of data fusion has been standardized by the Joint Directors of Labora- tories (JDL) Data Fusion Group, and this group maintains a Data Fusion Model. In this model, data fusion is divided into 5 levels as shown in Table1 . Note that SAW is Level 2 data fusion in this model.... ..."

Table 5. Data fusion retrieval runs with PRF.

in Dublin City University at CLEF 2004: Experiments with the ImageCLEF St. Andrew's Collection
by Gareth J. F. Jones, Declan Groves, Anna Khasin, Adenike Lam-adesina, Bart Mellebeek, Andy Way 2005
"... In PAGE 5: ... we have previously successfully used data fusion to combine the output of multiple topic translations in CLIR for news retrieval in CLEF 2001 [4]. Table5 shows results... ..."
Cited by 2

Table 6. Evaluation of various data fusion strategies

in Summary Report on the TREC-2003 Experiment: Genomic and Web Searches
by Jacques Savoy, Yves Rasolofo, Laura Perret
"... In PAGE 4: ...Table 6. Evaluation of various data fusion strategies Table6 shows the results of combining the Prosit and quot;dtu-dtn quot; search models, using both stemmers... ..."

Table 10. MAP with various data fusion schemes

in Comparative Study of Monolingual and Multilingual Search Models for Use with Asian Languages
by Jacques Savoy 2005
"... In PAGE 15: ...Table 10. MAP with various data fusion schemes Table10 shows the mean average precision (MAP) obtained from the Chinese, Japanese and Korean collections, for each of the T, D and TDNC queries. The top part of this table shows the individual performances of various retrieval models used in our data fusion experiments.... In PAGE 15: ... Moreover, linear combinations ( SumRSV ) usually resulted in good performance, and the Z-score scheme tended to produce the best performance. As shown in Table10 under the heading Z-scoreW , we attached a weight of 2 to the Prosit model, 1.5 to ... ..."
Cited by 2

Table 5: Baseline retrieval results for Data Fusion.

in Exeter at CLEF 2001: Experiments with Machine
by Translation For Bilingual, Gareth J. F. Jones, Adenike M. Lam-adesina 2002
Cited by 10

TABLE I Comparison between data and decision fusion

in Hybrid Data and Decision Fusion Techniques for Model-Based Data Gathering in Wireless Sensor Networks
by Lorenzo A. Rossi, Bhaskar Krishnamachari, C. -c. Jay Kuo

Table 1 Levels of Modeling and Data Fusion

in Intelligent Robots and Systems. Elsevier, 1994. Visual Recognition of Obstacles on Roads
by Uwe Regensburger, Volker Graefe

Table 1. Overview of Fusion Techniques

in NOVEL CLASSIFIER FUSION APPROAHCES FOR FAULT DIAGNOSIS IN AUTOMOTIVE SYSTEMS
by Kihoon Choi, Satnam Singh, Anuradha Kodali, Krishna R. Pattipati
"... In PAGE 2: ... This work has produced numerous techniques, which can be decomposed into five categories: classifier selection, combination of classifier outputs, sampling of classifier training data, manipulation of classifier outputs, and classifier feature selection. Classifier fusion techniques are categorized in Table1 , and a brief explanation of each technique follows. Classifier Selection Classifier selection endeavors to choose the best classifier for a given task.... ..."

Table 2 shows the MRPS numbers obtained for a test duration of 30 seconds on the 6-node Meiko. For small files, the MRPS can reach 45 for a single node, but only 9 for 1.5MB files. We also conducted a test for a duration of 120 seconds, in which MRPS dropsto4forasinglenode. Themulti-nodeservercansignificantly speedup the MRPS as shown in Table 2. We also tested the MRPS on the workstations clustered by the Ethernet. Effective bandwidth of our Ethernet is much smaller than the CS-2 Elan network, the MRPS for processing 1.5 MB files is about twice as small as on the Meiko.

in Scalability Issues for High Performance Digital Libraries on the World Wide Web
by Daniel Andresen, Tao Yang, Omer Egecioglu, Oscar H. Ibarra, Terence R. Smith 1996
"... In PAGE 8: ... File sizes in bytes 1K 1.5M Single server 45 9 Multi-node server 82 45 Table2 . MRPS for a test duration of 30s on six nodes clustered by Meiko CS-2 Elan.... ..."
Cited by 19

Table 2 shows the MRPS numbers obtained for a test duration of 30 seconds on the 6-node Meiko. For small files, the MRPS can reach 45 for a single node, but only 9 for 1.5MB files. We also conducted a test for a duration of 120 seconds, in which MRPS dropsto4forasinglenode. Themulti-nodeservercansignificantly speedup the MRPS as shown in Table 2. We also tested the MRPS on the workstations clustered by the Ethernet. Effective bandwidth of our Ethernet is much smaller than the CS-2 Elan network, the MRPS for processing 1.5 MB files is about twice as small as on the Meiko.

in Scalability issues for high performance digital libraries on the World Wide Web
by Daniel Andresen, Tao Yang, Omer Egecioglu, Oscar H. Ibarra, Terence R. Smith 1996
"... In PAGE 8: ... File sizes in bytes 1K 1.5M Single server 45 9 Multi-node server 82 45 Table2 . MRPS for a test duration of 30s on six nodes clustered by Meiko CS-2 Elan.... ..."
Cited by 19
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