Oommen,B.J. and Zhang,K. (1996) The normalized string editing problem revisited. IEEE Trans. Pattern Anal. Mach. Intell., 18, 669--672.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....in O(mn) time [13, 17, 5] or O(mn= log n) time if the weights are rational [8] In order for NED computations to be advantageous, the computational complexity of an algorithm for the latter should not significantly exceed these. There are several algorithms to compute NED, both sequential [7, 11, 16] and parallel [4] Observing that the length of an edit sequence lies in the range m and m n inclusive, an O(mn 2 ) time dynamic programming algorithm can be developed for this problem [7] Furthermore, it has been noted that NED can be formulated as a special case of constrained edit distance ....

.... [4] Observing that the length of an edit sequence lies in the range m and m n inclusive, an O(mn 2 ) time dynamic programming algorithm can be developed for this problem [7] Furthermore, it has been noted that NED can be formulated as a special case of constrained edit distance problems [11]. By adapting the techniques used for the constrained edit distance problems, NED can be computed in O(smn) time where s is the number of substitutions in an optimal edit sequence [11] But since s can be as large as n, the worst case time complexity remains O(mn 2 ) Another approach for NED ....

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B. J. Oommen and K. Zhang. The normalized string editing problem revisited. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(6):669--672, June 1996.

....1993) but requires cubic time and quadratic space. Various parallel algorithms for this problem were developed by Egecioglu and Ibel (1996) We want to emphasize the difference between the normalized local alignment and the previously studied normalized edit distance problem. The algorithms by Oommen and Zhang (1996), Vidal et al. 1995) Arslan and Egecioglu (1999) Arslan and Egecioglu (2000) do not aim to satisfy a constraint on the length, therefore they cannot directly be adapted to the the computation of normalized scores when lengths are restricted. In this paper, we propose a new practical algorithm ....

Oommen, B. J. and Zhang, K. (1996). The normalized string editing problem revisited, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(6), 669--672.

....in O(mn) time [13, 17, 5] or O(mn= log n) time if the weights are rational [8] In order for NED computations to be advantageous, the computational complexity of an algorithm for the latter should not significantly exceed these. There are several algorithms to compute NED, both sequential [7, 11, 16] and parallel [4] Observing that an edit sequence length lies in the range m and m n inclusive, an O(mn 2 ) time dynamic programming algorithm can be developed for this problem [7] Furthermore, it has been noted that NED can be formulated as a special case of constrained edit distance (CED) ....

.... [4] Observing that an edit sequence length lies in the range m and m n inclusive, an O(mn 2 ) time dynamic programming algorithm can be developed for this problem [7] Furthermore, it has been noted that NED can be formulated as a special case of constrained edit distance (CED) problems [11]. By adapting the techniques used for CED, NED can be computed in O(smn) time where s is the number of substitutions in an optimal edit sequence [11] But since s can be as large as n, the worst case time complexity remains O(mn 2 ) Another approach for NED computation uses fractional ....

[Article contains additional citation context not shown here]

B. J. Oommen and K. Zhang. The normalized string editing problem revisited. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(6):669--672, June 1996.

....in O(mn) time [14, 18, 6] or O(mn= log n) time if the weights are rational [9] In order for NED computations to be advantageous, the computational complexity of an algorithm for the latter should not significantly exceed these. There are several algorithms to compute NED, both sequential [8, 12, 17] and parallel [5] Observing that an edit sequence length lies in the range m, and m n inclusive, an O(mn 2 ) time dynamic programming algorithm can be developed for this problem [8] Furthermore, it has been noted that NED can be formulated as a special case of constrained edit distance (CED) ....

.... [5] Observing that an edit sequence length lies in the range m, and m n inclusive, an O(mn 2 ) time dynamic programming algorithm can be developed for this problem [8] Furthermore, it has been noted that NED can be formulated as a special case of constrained edit distance (CED) problems [12]. By adapting the techniques used for CED, NED can be computed in O(smn) time where s is the number of substitutions in an optimal edit sequence [12] But since s can be as large as n, the worst case time complexity remains O(mn 2 ) Another approach for NED computation uses fractional ....

[Article contains additional citation context not shown here]

B.J. Oommen, K. Zhang. The Normalized String Editing Problem Revisited. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18:6:669--672, June 1996.

....deletion and insertion operations of the individual symbols of the alphabet. In the literature, these operations are the most popular, because the general string editing problem has been studied using these operations Optimal and Information Theoretic Syntactic Pattern Recognition. Page 6 [3,4,5,6,7,8,9,18,19,20,21,22,23,24,25,26,27,35,36,37,38,39,40,41,42,43], and furthermore, these operations can also be used to study problems involving subsequences and supersequences [6,27,37,38,39] Also, since most errors found in text based systems are substitution, deletion and insertion errors (and even the common transposition error can be modelled easily as a ....

.... deletion and insertion errors (and even the common transposition error can be modelled easily as a sequence of a deletion and insertion error) systems which correct these errors have been designed to recognize noisy strings and substrings (see above references) and even noisy subsequences [24,25,41,44]. Viewed from the perspective of edit operations, our model is a distant relative of the Viterbi algorithms [12,16,31,35] I.3 Channels as Hidden Markov Models Recently, there has been a lot of research involving the use of Hidden Markov Models (HMM) to solve a variety of modelling and data ....

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B. J. Oommen and K. Zhang, The normalized string editing problem revisited, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.PAMI-18:669-672 (1996).

....Remarks : i) In the above algorithm the updating was performed using arrays. For large values of M and N it is more efficient to use pointers, in which case the updating of Wes0 from Wes1 is trivial. By using techniques analogous to those used in the computation of the Normalized Edit Distance [41] it is easy to do some fine tuning to achieve the computation with a single quadratic array. We omit these details here in the interest of brevity. Optimal and Information Theoretic Syntactic Pattern Recognition. Page 20 (ii) A note about the modus operandus of the proof of computing Pr[Y U] ....

B. J. Oommen and K. Zhang, "The Normalized String Editing Problem Revisited". To appear in the IEEE Transactions on Pattern Analysis and Machine Intelligence.

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Oommen,B.J. and Zhang,K. (1996) The normalized string editing problem revisited. IEEE Trans. Pattern Anal. Mach. Intell., 18, 669--672.

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Oommen, B., Zhang, K.: The normalized string editing problem revisited. IEEE Trans. on PAMI 18 (1996) 669-672

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Oommen, B., Zhang, K.: The normalized string editing problem revisited. IEEE Trans. on PAMI 18 (1996) 669-672

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