A. Apostolico and Z. Galil. Pattern matching algorithms. Oxford University Press, 1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....STJ and TMJ, simple queries: QS1 QS6 0 8 16 24 QC1 QC2 Time (in STJ D2 STJ A2 TMJ D2 TMJ A2 Figure 11. STJ and TMJ, complex queries: QC1, QC2 5 Related Work Matchings between pairs of trees in memory has been a topic of study in the algorithms community for a long time (e.g. see [3] and references therein) The algorithms developed deal with many variations of the problem but unfortunately are of high complexity and always assume that trees are entirely memory resident. The problem also has been considered in the programming language community, as it arises in various ....

A. Apostolico and Z. Galil. Pattern matching algorithms. Oxford University Press, 1997.

....are. The metrics used to measure the di#erence usually are heuristic and are application dependent. For example, in image template matching [20, 25] i=1 (a i b i ) and are often used to measure the di#erence between two sequences a and b. In DNA sequence matching [22] edit distance [3, 10] makes more sense than the above measurements; edit distance measures the cost of transforming one given sequence to another given sequence, and its special case, longest common subsequence is used to measure how similar two sequences are. Solving approximate pattern matching problems under such ....

A. Apostolico and Z. Galil, editors. Pattern Matching Algorithms. Oxford University Press, 1997.

....two targets are. The metrics used to measure the di erence usually are heuristic and are application dependent. For example, in image template matching [58, 64] ja i b i j are often used to measure the di erence between two sequences a and b. In DNA sequence matching [60] edit distance [6, 34] makes more sense than the above measurements; edit dis 111 tance measures the cost of transforming one given sequence to another given sequence, and its special case, longest common subsequence is used to measure how similar two sequences are. Solving approximate pattern matching problems ....

A. Apostolico and Z. Galil, editors. Pattern Matching Algorithms. Oxford University Press, 1997.

....two targets are. The metrics used to measure the di erence usually are heuristic and are application dependent. For example, in image template matching [12, 17] and ja i b i j are often used to measure the di erence between two sequences a and b. In DNA sequence matching [13] edit distance [1, 5] makes more sense than the above measurements; edit distance measures the cost of transforming one given sequence to another given sequence, and its special case, longest common subsequence is used to measure how similar two sequences are. Solving approximate pattern matching problems within the ....

A. Apostolico and Z. Galil, editors. Pattern Matching Algorithms. Oxford University Press, 1997.

....the set fjxj; T = xPyg. The complexity of this problem is O(u) in the worst case and O(u log (m) m) on average (where the characters are independent and uniformly distributed over an alphabet of size ) and there exist algorithms achieving both time complexities using O(m) extra space [CR94, AG97] A generalization of the basic string matching problem is approximate string matching: an error threshold k is also given as input, and we want to report all the ending positions of text substrings which match the pattern after performing a number of operations on them whose total cost cannot ....

A. Apostolico and Z. Galil. Pattern Matching Algorithms. Oxford University Press, Oxford, UK, 1997.

....the documents will produce inaccurate results. As a result, we need to cluster documents by their structural similarity before we can begin to re engineer the overall structure for each cluster of documents. Clustering can be applied on Document Trees based on the Tree Pattern Matching approach [2] or it can be coupled with the semantic tagging of HTML documents (since the semantic tagging process would already provide clues on the structure of each document instance) Phase II. Structure Discovery: The Structure Discovery phase takes a cluster of structurally similar Document Trees as ....

....contains the following attributes: TagName: The tag carried by the element represented by the node. Note every node in the Spanning Graph carries a unique TagName. ArtList: A list of attributes for the corresponding ele 69 DoclD = syxml img (a) 5] tl [1] 3,4] Spanning Graph M 1 [2] note map ref total map ref land crdinates location DoclD = u img lation (b) cotainential shelf [15] territorial sea [14] 12] ten itofial sea [9] water , 10 [5,25] water 5 . total 1,21 9.29 [11,31 ] map ref map ref Nnd 1,21 crdinates [1,21] 3,4,23,24] crdinates ....

[Article contains additional citation context not shown here]

A. Apostolico and Z. Galil. Pattern Matching Algorithms. Oxford University Press, 1997.

....String matching is a well studied problem and has applications in a wide variety of fields. Recently, various modifications of the exact string matching problem have been considered. Applications in computational biology and computer vision have motivated the study of approximate string matching [5, 6], where di#erent matching relations like swapped matching [2] don t cares [8] and overlap matching [3] have been proposed. There is another interesting variation of exact string matching, which we call context sensitive string matching. Here, the pattern is allowed to have variables and the ....

A. Apostolico and Z. Galil (eds.). Pattern Matching Algorithms. Oxford Univ. Press, 1997.

....shown in Figure 12. From the figure we see that STJ D2 has the highest performance once again, since it is never has to spool nodes to intermediate files. 5 Related Work Matchings between pairs of trees in memory has been a topic of study in the algorithms community for a long time (e.g. see [3] and references therein) The algorithms developed deal with many variations of the problem but unfortunately are of high complexity and always assume that trees are entirely memory resident. The problem also has been considered in the programming language community, as it arises in various type ....

A. Apostolico and Z. Galil. Pattern Matching Algorithms. Oxford University Press, 1997.

....consuming. But fortunately, most suspects can be eliminated by matching only a few bits. Because of this, a linear search through the bit lists takes less time than constructing a hash table. Substantial performance gains can also be achieved by using more sophisticated pattern matching algorithms [4]. If both Wrong Gate and Misplaced Wire correction types fail to provide a solution, we look for an Extra Missing Gate type of Error. Here, we try to synthesize a line from the existing lines in the network, making use of a new gate to match the required bit list of l. In our current ....

A. Apostolico and Z. Galil, Pattern Matching Algorithms, New York, NY: Oxford University Press, 1997.

No context found.

A. Apostolico and Z. Galil (Eds.), Pattern Matching Algorithms, Oxford University Press, New York (1997).

....other applications of probabilistic and statistical sequence analysis, access to the widest repertoire of models and scores is the crucial asset in the formulation, test and fine tuning of hypotheses. 2 Preliminaries We use standard concepts and notation about strings, for which we refer to [4, 3, 5]. For a substring y of a text x, we denote by f(y) the number of occurrences of y in x. We have f(y) pos x (y) endpos x (y) where pos x (y) is the start set of starting positions of y in x and endpos x (y) is the similarly defined end set. Clearly, for any extension uyv of y, ....

....to the nodes of a corresponding automaton or word graph, which becomes thereby the natural support for our statistical tables. The table takes linear space, since the number of classes is linear in x . The automata themselves are built by classical algorithms, for which we refer to, e.g. [3, 5, 8] with their quoted literature, or easy adaptations thereof. The graph for l , for instance, is the compact subword tree T x of x, whereas the graph for r is the dawg, or directed acyclic word graph D x , for x. The graph for is the compact version of the the dawg. These data structures ....

Apostolico, A., and Galil, Z., Eds. Pattern matching algorithms. Oxford University Press, New York, NY, 1997.

No context found.

Apostolico, A., and Z. Galil (Eds.), Pattern Matching Algorithms, Oxford University Press, New York (1997).

No context found.

A. Apostolico and Z. Galil. Pattern matching algorithms. Oxford University Press, 1997.

No context found.

Apostolico, A., and Galil, Z. 1997. Pattern Matching Algorithms. New York , NY: Oxford University Press. Asada, M.; Kitano, H.; Noda, I.; and Veloso, M.

No context found.

A. Apostolico and Z. Galil. Pattern Matching Algorithms. Oxford University Press, 1992.

No context found.

A. Apostolico and Z. Galil. Pattern Matching Algorithms. Oxford University Press, 1992.

No context found.

A. Apostolico and Z. Galil. Pattern matching algorithms. Oxford University Press, 1997.

No context found.

A. Apostolico and Z. Galil, editors. Pattern Matching Algorithms. Oxford University Press, New York, NY, 1997.

No context found.

A. Apostolico and Z. Galil. Pattern Matching Algorithms. Oxford University Press, ISBN 0-19-611367-5, 1997.

No context found.

A. Apostolico and Z. Galil (editors). Pattern Matching Algorithms. Oxford University Press, 1997.

No context found.

APOSTOLICO A., GALIL Z., Eds., Pattern Matching Algorithms, Oxford University Press, 1997.

No context found.

A. Apostolico and Z. Galil, editors. Pattern Matching Algorithms. Oxford University Press, 1997.

No context found.

A. Apostolico and Z. Galil, editors. Pattern Matching Algorithms. Oxford University Press, 1997.

No context found.

A. Apostolico and Z. Galil. Pattern matching algorithms. Oxford University Press, 1997.

No context found.

A. Apostolico and Z. Galil. Pattern Matching Algorithm. Oxford University Press, New York, 1997.

First 50 documents

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC