Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
A scalable technique for best-match retrieval of sequential information using metrics-guided search
Journal of Information Science
Efficient Error-Correcting Viterbi Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast algorithms for sorting and searching strings
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
The analysis of hybrid trie structures
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Offline General Handwritten Word Recognition Using an Approximate BEAM Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
An Approach to Designing Very Fast Approximate String Matching Algorithms
IEEE Transactions on Knowledge and Data Engineering
Tries for Approximate String Matching
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Algorithms for Cache-Efficient Trie Search
ALENEX '99 Selected papers from the International Workshop on Algorithm Engineering and Experimentation
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Stochastic Error-Correcting Parsing for OCR Post-Processing
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Dictionary matching and indexing with errors and don't cares
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
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This paper [5] deals with the problem of estimating, using enhanced AI techniques, a transmitted string X* by processing the corresponding string Y, which is a noisy version of X*. We assume that Y contains substitution, insertion and deletion errors, and that X* is an element of a finite (possibly large) dictionary, H. The best estimate X+ of X* is defined as that element of H which minimizes the Generalized Levenshtein Distance D(X, Y) between X and Y, for all X ∈ H. In this paper, we show how we can evaluate D(X, Y) for every X ∈ H simultaneously, when the edit distances are general and the maximum number of errors is not given a priori, and when H is stored as a trie. We first introduce a new scheme, Clustered Beam Search (CBS), a heuristic-based search approach that enhances the well known Beam Search (BS) techniques [33] contained in Artificial Intelligence (AI). It builds on BS with respect to the pruning time. The new technique is compared with the Depth First Search (DFS) trie-based technique [36] (with respect to time and accuracy) using large and small dictionaries. The results demonstrate a marked improvement up to (75%) with respect to the total number of operations needed on three benchmark dictionaries, while yielding an accuracy comparable to the optimal. Experiments are also done to show the benefits of the CBS over the BS when the search is done on the trie. The results also demonstrate a marked improvement (more than 91%) for large dictionaries.