K. Zhang and D. Shasha, Simple fast algorithms for the editing distance between trees and related problems, SIAM Journal on Computing 18, 6 (1989), pp. 1245--1262. 25  Home/Search Document Not in Database Summary Related Articles Check |
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Pseudoknots in RNA Secondary Structures - Lyngsų, Pedersen (2000) (3 citations) (Correct)
.... to rst handle one base pair and then the other (if they are nested) or handle them independently (if they are disjoint) The pseudoknot restriction is thus crucial in algorithms for e.g. structure prediction [1, 3, 6, 11, 19] partition function calculations [5] comparing secondary structures [18], and simultaneous alignment and structure prediction of RNA sequences [2, 12] In the following we will exemplify this by giving a brief summary of an algorithm of the mfold type for secondary structure prediction. The summary is also aimed at introducing the terminology we will use in section 3. ....
K. Zhang and D. Shasha. Simple fast algorithms for the editing distance between trees and related problems. SIAM Journal on Computing, 18(6):12451262, 1989.
On Integration of Parsing and Tree Matching Schemes - Ferro, Souto, Pena (2000) (Correct)
....the rest of the stack. This transition is applicable to the configuration resulting of the first one, but also on those to be generated and sharing the same syntactic structure, as shown in Fig. 1. 3 A DYNAMIC FRAME FOR APPROXIMATE TREE MATCHING We introduce the Zhang and Shasha s approach [Zhang Shasha 1989] to determine the distance between two trees as measured by the number of edit operations needed to transform one tree into the other. 3.1 The operational model Given trees, T 1 and T 2 , we define an edit operation as a pair a b; a 2 labels(T 1 ) f g; b 2 labels(T 2 ) f g; a; b) 6= ....
....l 1 )min(d 2 ; l 2 ) and the space complexity is O(n 1 n 2 ) both in the worst case, where n i is the number of nodes in the tree T i , d i is the depth of T i and l i is the number of leaves in T i , i = 1; 2. 4 RELATING PARSING AND TREE MATCHING The major question of previous related works [Zhang Shasha 1989], is the tree distance algorithm itself. However, in dealing with information retrieval often parsing and tree to tree correction are topologically related. Typically, this relation is the case when we deal with unrestricted natural language texts, including ambiguous sentences, and the syntactic ....
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K. Zhang and D. Shasha, 1989. Simple fast algorithms for the editing distance between trees and related problems. In SIAM Journal of Computing, volume 18, pages 1245-1262.
Pseudoknots in RNA Secondary Structures - Lyngsų, Pedersen (2000) (2 citations) (Correct)
.... us to rst handle one base pair and then the other (if they are nested) or handle them independently (if they are disjoint) The pseudoknot restriction is thus crucial in algorithms for e.g. structure prediction [1,3,6,11,19] partition function calculations [5] comparing secondary structures [18], and simultaneous alignment and structure prediction of RNA sequences [2, 12] In the following we will exemplify this by giving a brief summary of an algorithm of the mfold type for secondary structure prediction. The summary is also aimed at introducing the terminology we will use in section 3. ....
K. Zhang and D. Shasha. Simple fast algorithms for the editing distance between trees and related problems. SIAM Journal on Computing, 18(6):12451262, 1989.
A Tree-Edit-Distance Algorithm for Comparing Simple.. - Klein, Tirthapura.. (2000) (3 citations) (Correct)
....trees addresses the computation of distance between ordered, rooted trees. The comparison of rooted trees arises in applications where hierarchies must be represented, e.g. parse trees and image decomposition. Tai [8] gave the rst algorithm for edit distance in such trees. Zhang and Shasha [11] gave a faster and more space ecient algorithm. The space required by the latter is O(n 1 n 2 ) where n 1 and n 2 denote the number of nodes in the two trees being compared. The time required is O(n 1 n 2 1 2 ) where i is a parame J 4 4 4 J J 4 (a) b) J J J 4 J J (c) J ....
....v i that lies between the top of l i and top of r i . l i and r i could take 2n i values each and for a given l i and r i , v i could take atmost d i values. Hence, a straightforward analysis yields a time complexity of O(n 2 1 n 2 2 d 1 d 2 ) For the space complexity we observe (as in [11]) that we do not have to store the solution to every subproblem all the time and can reuse the space. This leads to a space requirement of O(n 1 n 2 ) for the permanent array (i.e for the subproblems we need to store throughout the computation) and the rest of the computation can also be ....
K. Zhang and D. Shasha, \Simple fast algorithms for the editing distance between trees and related problems, SIAM Journal on Computing 18 (1989), pp. 1245-1262.
Finding Largest Subtrees and Smallest Supertrees - Gupta, Nishimura (1998) (7 citations) (Correct)
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K. Zhang and D. Shasha, Simple fast algorithms for the editing distance between trees and related problems, SIAM Journal on Computing 18, 6 (1989), pp. 1245--1262. 25
Some uses of Fuzzy logic in multimedia databases querying - Dubois, Prade, Sèdes (1999) (2 citations) (Correct)
No context found.
ZS89, Zhang K. and Shasha D., Simple fast algorithms for the editing distance between trees and related problems. SIAM Journal of Computing, 18(6):1245-1262, 1989.
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