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Table 1. Symbols in the context of k-nearest neighbor search

in Optimal Multi-Step k-Nearest Neighbor Search
by Thomas Seidl, Hans-Peter Kriegel

Table 1. Symbols in the context of k-nearest neighbor search

in Optimal Multi-Step k-Nearest Neighbor Search
by Thomas Seidl, Hans-peter Kriegel

Table 1. Matching of the subjects in target and nearest neighbors

in On Effective Conceptual Indexing and Similarity Search in Text Data
by Charu C. Aggarwal, Philip S. Yu 2001
"... In PAGE 7: ... Specifically, we computed the p = 20 closest neighbors to the target, and calculated the percentage of neighbors from the same or a hierarchically related class. The re- sults are presented in Table1 . The first column indicates the class label of the target document; whereas the second and third columns indicate some statistics of the class distri- butions of the search results for the textual and conceptual neighbors respectively for all levels of the Y ahoo! hierar- chy which are related to the target.... In PAGE 7: ... Clearly the percentage of matching neighbors would always be higher while trying to make a partial match with a hierarchically related node. The values reported in each entry of Table1 are determined by averag- ing over all targets in the corresponding Y ahoo! class. It is also apparent that it is goodness for these accuracy numbers to be as high as possible, if we assume that Y ahoo! class labels reflect topical behavior well.... In PAGE 7: ... It is also apparent that it is goodness for these accuracy numbers to be as high as possible, if we assume that Y ahoo! class labels reflect topical behavior well. As illustrated in Table1 , an exact match between the class labels of the target document and the nearest neighbors was found a very small percentage of the time for the textual nearest neighbor. We note that we are only using the match- ing percentage of class labels of an unsupervised similar- ity search procedure in order to demonstrate the qualitative advantages of conceptual similarity.... ..."
Cited by 5

Table 1. Matching of the subjects in target and nearest neighbors

in On Effective Conceptual Indexing and Similarity Search in Text Data
by Charu C. Aggarwal 2001
"... In PAGE 7: ... Specifically, we computed the p = 20 closest neighbors to the target, and calculated the percentage of neighbors from the same or a hierarchically related class. The re- sults are presented in Table1 . The first column indicates the class label of the target document; whereas the second and third columns indicate some statistics of the class distri- butions of the search results for the textual and conceptual neighbors respectively for all levels of the Y ahoo! hierar- chy which are related to the target.... In PAGE 7: ... Clearly the percentage of matching neighbors would always be higher while trying to make a partial match with a hierarchically related node. The values reported in each entry of Table1 are determined by averag- ing over all targets in the corresponding Y ahoo! class. It is also apparent that it is goodness for these accuracy numbers to be as high as possible, if we assume that Y ahoo! class labels reflect topical behavior well.... In PAGE 7: ... It is also apparent that it is goodness for these accuracy numbers to be as high as possible, if we assume that Y ahoo! class labels reflect topical behavior well. As illustrated in Table1 , an exact match between the class labels of the target document and the nearest neighbors was found a very small percentage of the time for the textual nearest neighbor. We note that we are only using the match- ing percentage of class labels of an unsupervised similar- ity search procedure in order to demonstrate the qualitative advantages of conceptual similarity.... ..."
Cited by 5

Table 1: Queries in Adaptivity Experiment, with Query Arrival Times. Workload Arrival Time (Seconds) 1. select index from S where a gt; 10 0 2. select index from S where b gt; 30 5 3. select index from S where c gt; 50 10 4. select index from S where d gt; 70 15 5. select index from S where e gt; 90 20

in Query Processing for Streaming Sensor Data
by Samuel Madden
"... In PAGE 6: ... Several experiments were run to measure the ability of CACQ to adapt to changes in query workload. One of them is summarized below: Table1 shows five simple queries and their time of arrival in the system. All queries are over a data stream S, each tuple of which contains six fields: an index, and five randomly generated fields a,b,c,d,e and f, each of which is randomly and uniformly distributed over the range [0.... ..."

Table: I. Nearest Neighbor

in unknown title
by unknown authors

Table: I. Nearest Neighbor

in unknown title
by unknown authors 2008

Table 7. Page accesses per search for eight-nearest neighbor search for clustered data of dimension 30

in Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances
by Ada Wai-chee Fu, Polly Mei-shuen Chan, Yin-ling Cheung, Yiu Sang Moon 2000
"... In PAGE 19: ... We also measured the page accesses to see how the three methods affect the access cost of the vp-tree. Table7 dis- plays the results. All three methods in general make fewer page accesses than the original vp-tree structure.... ..."
Cited by 17

Table 7. A sample of reciprocally nearest

in Noun Classification From Predicate.argument Structures
by Donald Hindle

Table 2.1: Searching performance of some nearest-neighbor search algo- rithms. [1]

in Fuzzy Clustering for Content-based Indexing in Multimedia Database
by Ho-Yin Yue, Ho-yin Yue
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