Ukkonen, E. (1985), `Algorithms for approximate string matching', Information and Control Vol 64. Home/Search Document Not in Database Summary Related Articles Check |
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Faster Bit-parallel Approximate String Matching - Hyyrö, Navarro (2002) (Correct)
....2 f Gamma1; 0; 1g Deltav i;j = M i;j Gamma M i Gamma1;j 2 f Gamma1; 0; 1g Deltad i;j = M i;j Gamma M i Gamma1;j Gamma1 2 f0; 1g the horizontal, vertical, and diagonal differences among consecutive cells. Their range of values come from the properties of the dynamic programming matrix [19]. We present a version [8] that differs slightly from that of [10] Although both perform the same number of operations per text character, the one we present is easier to understand and more convenient for our purposes. Let us introduce the following boolean variables. The first four refer to ....
E. Ukkonen. Algorithms for approximate string matching. Information and Control, 64:100--118, 1985.
Edit-Distance of Weighted Automata - Mohri (2002) (1 citation) (Correct)
....where jx i j, i = 1; 2, denotes the length of the string x i [32] The algorithm is a special instance of a single source shortest paths algorithm applied to a directed graph expanded dynamically. Ukkonen improved that algorithm by reducing the size of the directed graph that needs to be expanded [31]. The complexity of his algorithm is O(dmax fjx 1 j; jx 2 jg) where d is the edit distance of x 1 and x 2 . The algorithm is more ecient for strings such that the distance d is small with respect to jx 1 j and jx 2 j. We refer the reader to [6, 12] for general surveys of edit distance and ....
Esko Ukkonen. Algorithms for approximate string matching. Information and Control, 64:100-118, 1985.
Dynamic Maintenance of Web Indexes Using Landmarks - Lim, Wang, Padmanabhan, al. (2003) (3 citations) (Correct)
....index as an update. Consider the number of inverted index update oper ations (insert and delete postings) generated by our update SThe keyword with the smallest frequency. 3.2 The Complexity of the diff Operation The use of edit transcripts (dill output) is a key idea in our method. Ukkonen [20] has showed that an edit tran script of two documents with size rs and n words can be computed in O(D min n, rs ) time using O(D min n, space, where D is the minimum edit distance. The UNIX cliff program uses an output sensitive heuristic algorithm similar to that of [20] so that the running ....
....in our method. Ukkonen [20] has showed that an edit tran script of two documents with size rs and n words can be computed in O(D min n, rs ) time using O(D min n, space, where D is the minimum edit distance. The UNIX cliff program uses an output sensitive heuristic algorithm similar to that of [20], so that the running time is near linear when D is small. Since diff is near linear for small updates and the up dates are usual small between two consecutive samples [15] diff is not a bottleneck in the processing in most cases. If the diff operation did form a bottleneck, we could represent ....
E. Ukkonen. Algorithms for approximate string matching. Information and Control, 64, 100-118, 1985.
Faster Bit-parallel Approximate String Matching - Hyyrö, Navarro (2002) (1 citation) (Correct)
....is initialized at C i i and updated to C after reading text character T j using C i if (P i = T j ) then C i Gamma1 else 1 min(C i Gamma1 ; C i ; C i Gamma1 ) for all i 0, and hence we report every end position j where C i k. Several properties of the matrix M are discussed in [19]. The most important for us is that adjacent cells in M differ at most by 1, that is, both M i;j Gamma M i Sigma1;j and M i;j Gamma M i;j Sigma1 are in the range f Gamma1; 0; 1g. Also, M i 1;j 1 Gamma M i;j is in the range f0; 1g. Fig. 1 shows examples of edit distance computation and ....
....2 f Gamma1; 0; 1g Deltav i;j = M i;j Gamma M i Gamma1;j 2 f Gamma1; 0; 1g Deltad i;j = M i;j Gamma M i Gamma1;j Gamma1 2 f0; 1g the horizontal, vertical, and diagonal differences among consecutive cells. Their range of values come from the properties of the dynamic programming matrix [19]. We present a version [8] that differs slightly from that of [10] Although both perform the same number of operations per text character, the one we present is easier to understand and more convenient for our purposes. Let us introduce the following boolean variables. The first four refer to ....
E. Ukkonen. Algorithms for approximate string matching. Information and Control, 64:100--118, 1985.
Transposition Invariant String Matching - Mäkinen, Navarro, Ukkonen (2004) Self-citation (Ukkonen) (Correct)
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E. Ukkonen. Algorithms for approximate string matching. Information and Control 64(13):100118, 1985.
Algorithms for Transposition Invariant String Matching - Mäkinen, Navarro, Ukkonen (2002) Self-citation (Ukkonen) (Correct)
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E. Ukkonen. Algorithms for approximate string matching. Information and Control 64(13):100118, 1985.
Approximate Matching of Run-Length Compressed Strings - Mäkinen, Navarro, Ukkonen (2001) Self-citation (Ukkonen) (Correct)
....) time for searching a pattern of length m that is run length compressed to length m , in a run length compressed text of length n . We also study the LCS calculation. First, we give a greedy algorithm for the LCS that works in O(m ) time. Adapting the well known diagonal method [24], we are able to improve the greedy method to work in O(d ; m ) time, where d is the edit distance between the two strings (under insertions and deletions with the unit cost model) Then we present improvements for the greedy method for the LCS, which do not however a ect the worst case, ....
....time, and in equal letter boxes we can trace an optimal path to a corner in O(m ) time. Thus, we can calculate all the corner values in O(m ) time . It turns out that we can improve the greedy algorithm signi cantly by fairly simple means. We notice that the diagonal method of [24] can be applied, and yields an ) algorithm, where d = D ID (A; B) We also give other improvements Apostolico et al. 4] also gave a basic O(m ) algorithm for the LCS, which they then improved to O(m ) Their basic algorithm di ers from our greedy algorithm in that ....
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E. Ukkonen. Algorithms for approximate string matching. Information and Control 64(1-3):100-118, 1985.
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Ukkonen, E. (1985), `Algorithms for approximate string matching', Information and Control Vol 64.
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E. Ukkonen, Algorithms for approximate string matching., Information and Control, 64:100-118, 1985.
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Ukkonen, E.: Algorithms for approximate string matching. Inform. and Control 64, 1985, 100--118.
Bit-Parallel Approximate String Matching Algorithms with.. - Hyyrö (2003) (Correct)
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Esko Ukkonen. Algorithms for approximate string matching. Information and Control, 64:100--118, 1985.
Practical Methods for Approximate String Matching - Hyyrö (Correct)
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E. Ukkonen. Algorithms for approximate string matching. Information and Control, 64:100--118, 1985.
Increased Bit-Parallelism for Approximate String Matching - Hyyrö, Fredriksson, Navarro (Correct)
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E. Ukkonen. Algorithms for approximate string matching. Information and Control, 64(1--3):100--118, 1985.
Cellular Associative Neural Networks for Pattern Recognition - Orovas (1999) (Correct)
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E. Ukkonen. Algorithms for approximate string matching. Information and Control, 64:100--118, 1985.
String Averages and Self-Organizing Maps for Strings - Fischer, Zell (2000) (1 citation) (Correct)
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E. Ukkonen, Algorithms for approximate string matching, Information and Control, 64 (1985) 100-118
Increased Bit-Parallelism for Approximate String Matching - Hyyrö, Fredriksson, Navarro (Correct)
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E. Ukkonen. Algorithms for approximate string matching. Information and Control, 64(1--3):100--118, 1985.
Row-wise tiling for the Myers' bit-parallel approximate string.. - Fredriksson (Correct)
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E. Ukkonen. Algorithms for approximate string matching. Inf. Control, 64(1{ 3):100-118, 1985.
Bit-Parallel LCS-length Computation Revisited - Hyyrö (Correct)
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Esko Ukkonen. Algorithms for approximate string matching. Information and Control, 64:100-- 118, 1985.
Bit-Parallel Approximate String Matching Algorithms with.. - Hyyrö (Correct)
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Esko Ukkonen. Algorithms for approximate string matching. Information and Control, 64:100--118, 1985.
Maximal Common Subsequences and Minimal Common Supersequences - Fraser, Irving, Middendorf (1995) (4 citations) (Correct)
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E. Ukkonen. Algorithms for approximate string matching. Information and Control, 64:100--118, 1985.
Bit-parallel Witnesses and their Applications to Approximate.. - Hyyrö, Navarro (Correct)
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E. Ukkonen. Algorithms for approximate string matching. Information and Control, 64:100-- 118, 1985.
Increased Bit-Parallelism for Approximate String Matching - Hyyrö, Fredriksson, Navarro (Correct)
No context found.
E. Ukkonen. Algorithms for approximate string matching. Information and Control, 64(1--3):100--118, 1985.
Edit-Distance of Weighted Automata - Mohri (2002) (1 citation) (Correct)
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Esko Ukkonen. Algorithms for approximate string matching. Information and Control, 64:100-118, 1985.
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Ukkonen, E., Algorithms for approximate string matching. Information and Control 64 (1985) 100--118
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Esko Ukkonen, `Algorithms for approximate string matching', Information and Control, 64, 100-- 118 (1985).
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