Titlebook: Net Gain; Profit im Netz John Hagel,Arthur G. Armstrong Book 1999 Springer Fachmedien Wiesbaden 1999 Erfolg.Management.Markt.Marktpositioni

摘要記錄

n the field of neural indexing. By learning a single neural network model that approximates the .-nearest neighbor distance bounds for all points in a database, the storage complexity of the proposed index structure is reduced to . while the index is still able to guarantee exact query results. As s

Asseverate

John Hagel III,Arthur G. Armstrongn the field of neural indexing. By learning a single neural network model that approximates the .-nearest neighbor distance bounds for all points in a database, the storage complexity of the proposed index structure is reduced to . while the index is still able to guarantee exact query results. As s

解開

John Hagel III,Arthur G. Armstrongn the field of neural indexing. By learning a single neural network model that approximates the .-nearest neighbor distance bounds for all points in a database, the storage complexity of the proposed index structure is reduced to . while the index is still able to guarantee exact query results. As s

ECG769

John Hagel III,Arthur G. Armstrongr of distance calculations. We applied our technique in the Slim-tree and performed experiments over real data sets showing that the proposed technique is able to reduce the execution time of both range and .-nearest queries to at least half of the Slim-tree. Moreover, this technique is general to b

確定的事

John Hagel III,Arthur G. Armstrongr of distance calculations. We applied our technique in the Slim-tree and performed experiments over real data sets showing that the proposed technique is able to reduce the execution time of both range and .-nearest queries to at least half of the Slim-tree. Moreover, this technique is general to b

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HUMP

Diuretic

轉(zhuǎn)向

寬大

John Hagel III,Arthur G. Armstrong solution..As dimensionality increases, the number of defined regions must increase, but the memory required for the exclusion calculation does not. We show that the technique gives excellent performance over the SISAP benchmark data sets, and most interestingly we show how increases in dimensionali