Algorithms And Data Structures

New PDF release: A 2E4-time algorithm for MAX-CUT

By Kulikov A. S.

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11 shows the performance of this overlapelimination refinement of QP-RIPPER and QP-SVM against the performance of their original versions without overlap elimination. Surprisingly, the overlapelimination refinement resulted in slightly degraded classification accuracy. A possible explanation for this phenomenon is that the original versions of QProber might actually benefit from probe overlap, since “double-counting” might help compensate for the low recall of some of the query probes. 2), we do not consider this QProber refinement further.

For Step 1, we use a random word from the approximately 100,000 words in our newsgroup collection. For Step 2, we use N = 4, which is the value that Callan et al. recommend in [CCD99]. Finally, for the termination condition in Step 4 we used both the termination conditions described in [CC01] and in [CCD99]. In [CC01] the algorithm terminates after the retrieval of 500 documents, while in [CCD99] the algorithm terminates when the vocabulary and frequency statistics associated with the sample document collection converge to a reasonably stable state.

Then we eliminated all the categories of the third level to create a shallower classification scheme (level=2). We repeated this process again, until our classification schemes consisted of one single node (level=0). Of course, the performance of all the methods at this point was perfect. 4 and τec = τc = 8 (the trends were the same for other threshold combinations as well). The results confirmed our earlier observations: QProber performs better than the other techniques for different depths, with only a smooth degradation in per- 2.

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A 2E4-time algorithm for MAX-CUT by Kulikov A. S.


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