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Table 5 : Expressions linking : query with adaptation

in Medical Dictionaries for Patient Encoding Systems: a Methodology
by C. Lovis, R. Baud, AM. Rassinoux, PA. Michel, JR. Scherrer
"... In PAGE 8: ...The source classification is analysed using the aggregated dictionary and a cross-link dic- tionary is built between the elements found in each aggregation group and the words found in the expressions of the classification ( Table5 ). Whereas the previous steps were done manually by a physician and a computer scientist, the linking is done automatically.... ..."

Table 8: Overall average non-interpolated precision for the short query and the adaptive linear model.

in Interactive Retrieval using IRIS: TREC-6 Experiments
by Robert G. Sumner, Jr., Kiduk Yang, Roger Akers, W. M. Shaw 1998
"... In PAGE 20: ...0463 0.0350 Table8 contains the results of our testing using the 31 TREC-5 topics and the short query. Only the adaptive linear model was tested because of its superior performance using the long query.... ..."
Cited by 14

Table 8: Overall average non-interpolated precision for the short query and the adaptive linear model.

in Interactive Retrieval using IRIS: TREC-6 Experiments
by Robert Sumner, Kiduk Yang, Roger Akers, W. M. Shaw 1998
"... In PAGE 20: ...0463 0.0350 Table8 contains the results of our testing using the 31 TREC-5 topics and the short query. Only the adaptive linear model was tested because of its superior performance using the long query.... ..."
Cited by 14

Table 5: Filters 1,2 vs. Filter 2 and nearest-neighbor vs. nearest-neighbor with location attributes. Diagnosis Image nearest-neighbor Multi-Hash + nearest-neighbor Multi-Hash + nearest-neighbor + Filters number

in ASSERT: A Physician-in-the-loop Content-Based Retrieval System for HRCT Image Databases
by Chi-ren Shyu, Carla Brodley, Avi Kak, Akio Kosaka, Alex M. Aisen, Lynn S. Broderick
"... In PAGE 37: ... Recall, the nal four images selected for retrieval are chosen from this set of candidate images. The third and the fourth columns of Table5 show the retrieval accuracy obtained by applying the nearest-neighbor search. This evaluation is based on the matching of PBRs directly.... ..."

Table 2. Nearest neighbor search - Hungarian method

in Robust matching of affinely transformed objects
by Herbert Suesse, Wolfgang Ortmann 2003
"... In PAGE 2: ... This was done by different levels of noise. In the Table2 the im- provement of the candidate matches can be seen. If we have regular objects the procedure works very well.... ..."
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Table 3.9: Random examples of Reciprocal Nearest Neighbors in the Semantic Space.

in Modeling Word Senses with Fuzzy Clustering
by Erik Velldal 2003
Cited by 1

Table 3.9: Random examples of Reciprocal Nearest Neighbors in the Semantic Space.

in Modeling word senses with fuzzy clustering
by Erik Velldal 2003
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Table 2. Sample profiles of nearest-neighbors search algorithm.

in An Efficient Computation-Constrained Block-Based Motion Estimation Algorithm for Low Bit Rate Video Coding
by Michael Gallant, Faouzi Kossentini 1999
"... In PAGE 4: ... Figure 5 illustrates the graceful computation degradation capability of the nearest-neighbors search algorithm for the sequence FOREMAN. The figure shows the rate-distortion performance of the full search algorithm and the nearest- neighbors search algorithm, for the basic, low-computation, and high-quality profiles outlined in Table2 . Clearly, even for difficult sequences, the high-quality profile can achieve a performance level that is close to that of the full search 20 25 30 35 40 45 50 55 60 65 30 30.... ..."
Cited by 12

Table 6: Nearest neighbor and distance to nearest neighbor

in Local Recoding by Maximum Weight Matching for Disclosure Control of Microdata Sets
by Akimichi Takemura 1999
"... In PAGE 5: ...Using Table 5 we can list closest households ( nearest neighbors ) from each household as shown in Table 6. From Table6 the average distance to nearest neighbors is calculated as 2.4.... ..."
Cited by 1

Table 6: Nearest neighbor and distance to nearest neighbor

in Local Recoding by Maximum Weight Matching for Disclosure Control of Microdata Sets
by Akimichi Takemura 1999
"... In PAGE 5: ... Using Table 5 we can list closest households ( nearest neighbors ) from each household as shown in Table 6. From Table6 the average distance to nearest neighbors is calculated as 2.4.... ..."
Cited by 1
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