Results 141 to 150 of about 3,338 (175)

Kernels based on weighted Levenshtein distance

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2005
In some real world applications, the sample could be described as a string of symbols rather than a vector of real numbers. It is necessary to determine the similarity or dissimilarity of two strings in many training algorithms. The widely used notion of similarity of two strings with different lengths is the weighted Levenshtein distance (WLD), which ...
Jianhua Xu, Xuegong Zhang
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Approximate Periods with Levenshtein Distance

2008
We present a new algorithm deciding for strings tand wwhether wis an approximate generator of twith Levenshtein distance at most k. The algorithm is based on finite state transducers.
Martin Simunek, Borivoj Melichar
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IRIS RECOGNITION USING ADABOOST AND LEVENSHTEIN DISTANCES

International Journal of Pattern Recognition and Artificial Intelligence, 2012
This paper presents an efficient IrisCode classifier, built from phase features which uses AdaBoost for the selection of Gabor wavelets bandwidths. The final iris classifier consists of a weighted contribution of weak classifiers. As weak classifiers we use three-split decision trees that identify a candidate based on the Levenshtein distance between ...
Joan Climent, Roberto A. Hexsel
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Computing the Levenshtein distance of a regular language

IEEE Information Theory Workshop, 2005., 2005
The edit distance (or Levenshtein distance) between two words is the smallest number of substitutions, insertions, and deletions of symbols that can be used to transform one of the words into the other. In this paper we consider the problem of computing the edit distance of a regular language (also known as constraint system), that is, the set of words
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Parallel Computations of Levenshtein Distances

1997
This chapter discusses parallel solutions for the string editing problem introduced in Chapter 5. The model of computation used is the synchronous, shared - memory machine referred to as PRAM and discussed also earlier in this book. The algorithms of this chapter are based on the CREW and CRCW variants of the PRAM.
A. Apostolico, M.J. Atallah
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Localizing Unordered Panoramic Images Using the Levenshtein Distance

2007 IEEE 11th International Conference on Computer Vision, 2007
This paper proposes a feature-based method for recovering the relative positions of the viewpoints of a set of panoramic images for which no a priori order information is available, along with certain structure information regarding the imaged environment.
Damien Michel, Antonis A. Argyros, Manolis I. A. Lourakis   +2 more
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Automatic keyword extraction with relational clustering and Levenshtein distances

Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 2002
Alternating cluster estimation (ACE) is a generalized clustering model. Relational ACE is a modification of ACE that can be used to cluster data which do not possess a clear numerical representation, but for which a meaningful relation matrix can be defined.
Thomas A. Runkler, James C. Bezdek
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Privacy preserving string comparisons based on Levenshtein distance

2010 IEEE International Workshop on Information Forensics and Security, 2010
Alice and Bob possess strings x and y of length m and n respectively and want to compute the Levenshtein distance L(x, y) between the strings under privacy and communication constraints. The Levenshtein distance, or edit distance, has a dynamic programming formulation that solves a series of minimum-finding problems.
Shantanu Rane, Wei Sun 0008
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Online Handwriting Recognition Using Levenshtein Distance Metric

2013 12th International Conference on Document Analysis and Recognition, 2013
In this article, we propose a novel scheme for online handwritten character recognition based on Levenshtein distance metric. Both shape and position information are considered in our feature representation scheme. The shape information is encoded by a string of quantized values of angular displacements between successive sample points along the ...
S. Dutta Chowdhury, Ujjwal Bhattacharya, Swapan K. Parui   +2 more
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Automated Test Scenario Selection Based on Levenshtein Distance

2010
Specification based testing involves generating test cases from the specification, here, UML. The number of automatically generated test scenarios from UML activity diagrams is large and hence impossible to test completely. This paper presents a method for selection of test scenarios generated from activity diagrams using Levenshtein distance.
Sapna P. G., Hrushikesha Mohanty
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