By Edward L Spitznagel

ISBN-10: 0030846935

ISBN-13: 9780030846939

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It is not numbered since it is not relevant. Then come 3, 4, 5 all relevant, while 6 is missing. Again 7 is relevant but 8 is not, then 9 and 10 are good. Now our algorithm has given us 10 suggestions, of which we find that 7 are relevant. 2. 634 since the last relevant documents are found much later, we see that the last one scores as number 141. This is still not too bad! We had 1033 documents to score. Let's take a final look at the lower half of Figure 3. There are many points along the y axis, which denote documents that are orthogonal to the projected query, and will be the last ones scored.

BOOKER, M. CONDLIFF, M. B. HOLT, A. J. PIERCE, S. J. Wu, Visualizing Text Data Sets, Computing in Science & Engineering 1(4): 26-35, 1999. [3] S. DEERWESTER, S. W. K. LANDAUER, AND R. HARSHMAN, Indexing by Latent Semantic Analysis, Journal of the American Society for Information Science 41(6):391-407, 1990. [4] S. DUMAIS, Improving the Retrieval of Information from External Sources, Behavior Research Methods, Instrument, & Computers 23:229-236, 1991. B. FRAKES AND R. BAEZA-YATES, Information Retrieval: Data Structures and Algorithms, Prentice-Hall, Englewood Cliffs, NJ, 1992.

Medline matrix, Query 13, upper half step j — 2, lower half step j = 12, numbers scores of relevant documents.

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Selected topics in mathematics by Edward L Spitznagel


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