We chose this method because it accounts for the temporal, spatial and sequential components of fixations and so overcomes limitations of existing string edit methods (Cristino et al., 2010).
To calculate structural similarity between hits, we used RNAdistance [26], which calculates a tree or string edit distance between RNA structures.
We propose three algorithms for string edit distance with duplications and contractions.
In this paper, we address the problem of string edit distance with the additional operations of duplication and contraction.
However in that case, it was hard-coded in the model in order to train finite-state string edit distance.
Firstly, they introduced a class of new kernels based on string edit distance, called edit kernels, to be used with SVM.
The single copy duplication model of minisatellite maps gave rise to a new variant of the string edit distance problem, Edit Distance with Duplications and Contractions (EDDC), which allows five edit operations: insertion, deletion, mutation, duplication and contraction.
Such needs are hardly fulfilled by the more traditional measures of sequence similarity and distance, like string edit and gene rearrangement, due to a mixture of epistemological and computational problems.
Module 1 is based on a classical spelling checker, it means that it is context-independent and simply measures a string-edit-distance between a misspelled word and a list of well-formed words.
Anderson, Anderson, Kingstone, and Bischof (2015) compared the ability of several scan path comparison methods to reveal similarities both within and between individuals looking at natural scenes and concluded that ScanMatch is a remarkable improvement on more simple method string-edit and linear distance methods.
