Titlebook: Combinatorial Pattern Matching; 8th Annual Symposium Alberto Apostolico,Jotun Hein Conference proceedings 1997 Springer-Verlag Berlin Heide

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書(shū)目名稱Combinatorial Pattern Matching影響因子(影響力)




書(shū)目名稱Combinatorial Pattern Matching影響因子(影響力)學(xué)科排名




書(shū)目名稱Combinatorial Pattern Matching網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Combinatorial Pattern Matching網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Combinatorial Pattern Matching被引頻次




書(shū)目名稱Combinatorial Pattern Matching被引頻次學(xué)科排名




書(shū)目名稱Combinatorial Pattern Matching年度引用




書(shū)目名稱Combinatorial Pattern Matching年度引用學(xué)科排名




書(shū)目名稱Combinatorial Pattern Matching讀者反饋




書(shū)目名稱Combinatorial Pattern Matching讀者反饋學(xué)科排名

jocular

Episode matching,ntain episode . as a subsequence. The respective optimization problem is to find the smallest number ., s.t. text . has a subword of length . which contains episode ...In this paper, we introduce a few efficient off-line as well as on-line algorithms for the entire problem, where by on-line algorith

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Creditee

On the complexity of pattern matching for highly compressed two-dimensional texts,ub-texts are defined in terms of smaller parts in a way similar to that of Lempel-Ziv compression for 1-dimensional texts, or in shortly described strings as in [9], or in hierarchical graphs described by context-free graph grammars. A given 2d-text . with many internal repetitions can have a hierar

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Spirometry

Spirometry

External inverse pattern matching,oal is to find a pattern . ∈ .. which is not a subword of . and which maximizes the sum of Hamming distances between . and all subwords of . of length .. We present an optimal . log σ)-time (where σ=∣Σ∣) algorithm for the external inverse pattern matching problem. This substantially improves the . l

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盤旋

A filter method for the weighted local similarity search problem,. of a pattern and subwords of a text whose lengths exceed a certain threshold. Moreover, arbitrary gap and substitution weights are allowed. In this paper, two new prefilter algorithms for the weighted local similarity search problem are presented. These overcome the disadvantages of a similar filt

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