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Efficient Voting Prediction for Pairwise Multilabel Classification
, 2009
"... The pairwise approach to multilabel classification reduces the problem to learning and aggregating preference predictions among the possible labels. A key problem is the need to query a quadratic number of preferences for making a prediction. To solve this problem, we extend the recently proposed QW ..."
Abstract
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Cited by 16 (6 self)
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QWeighted algorithm for efficient pairwise multiclass voting to the multilabel setting, and evaluate the adapted algorithm on several real-world datasets. We achieve an average-case reduction of classifier evaluations from n2 to n+dn log n, where n is the total number of labels and d is the average number
Some algorithmic aspects of the empirical likelihood method in survey sampling
- Statistica Sinica
, 2004
"... Abstract: Recent development of the empirical likelihood method in survey sampling has attracted attention from survey statisticians. Practical considerations for using the method in real surveys depend largely on the availability of simple and efficient algorithms for computing the related weights ..."
Abstract
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Cited by 12 (6 self)
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used design in survey practice. The idea of the q-weighted empirical likelihood approach is briefly introduced and the related algorithms are discussed. The proposed algorithms are tested in a limited simulation study and are shown to perform well. Key words and phrases: Newton-Raphson algorithm
Robinson-Schensted algorithm
"... We introduce a q-weighted version of the Robinson-Schensted (column insertion) algorithm which is closely connected to q-Whittaker functions (or Macdonald polynomials with t = 0) and reduces to the usual Robinson-Schensted algorithm when q = 0. The q-insertion algorithm is ‘randomised’, or ‘quantum’ ..."
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We introduce a q-weighted version of the Robinson-Schensted (column insertion) algorithm which is closely connected to q-Whittaker functions (or Macdonald polynomials with t = 0) and reduces to the usual Robinson-Schensted algorithm when q = 0. The q-insertion algorithm is ‘randomised’, or ‘quantum
Decremental Dynamic Connectivity
- In Proceedings of the 8th ACM-SIAM Symposium on Discrete Algorithms (SODA
, 1997
"... We consider Las Vegas randomized dynamic algorithms for on-line connectivity problems with deletions only. In particular, we show that starting from a graph with m edges and n nodes, we can maintain a spanning forest during m deletions in O(minfn 2 ; m log ng+ p nm log 2:5 n) expected total ti ..."
Abstract
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Cited by 19 (1 self)
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We consider Las Vegas randomized dynamic algorithms for on-line connectivity problems with deletions only. In particular, we show that starting from a graph with m edges and n nodes, we can maintain a spanning forest during m deletions in O(minfn 2 ; m log ng+ p nm log 2:5 n) expected total
