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Phonological Neighborhood Density and Type Modulate Visual Recognition of Mandarin Chinese: Evidence from Monosyllabic Words. [PDF]
Brain SciJiao Z, Zhou X, Chen W.
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deepNGS navigator: exploring antibody NGS datasets using deep contrastive learning. [PDF]
BioinformaticsMohammadiPeyhani H +4 more
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Constraints on Exchange Edits During Noisy-Channel Inference. [PDF]
Cogn SciBader M, Meng M.
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Learning string-edit distance [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998 http://www.cs.princeton.edu/~ristad/papers/pu-532-96.ps ...
Ristad, Eric Sven, Yianilos, Peter N.
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Biological Network Edit Distance
Journal of Computational Biology, 2016Interactions among biological entities contain more information than purely the similarities between the entities. For example, interactions between genes, and gene products, can be more informative than the sequence similarities of the genes involved.
Martin, McGrane, Michael A, Charleston
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2018 24th International Conference on Pattern Recognition (ICPR), 2018
In this paper, we present a novel distance metric called Segmentation Edit Distance (SED) and its use as a segmentation evaluation metric. In segmentation evaluation, the difference or distance of a test segmentation and the associated ground truth segmentation are measured in order to compare different algorithms.
Daniel Pucher, Walter G. Kropatsch
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In this paper, we present a novel distance metric called Segmentation Edit Distance (SED) and its use as a segmentation evaluation metric. In segmentation evaluation, the difference or distance of a test segmentation and the associated ground truth segmentation are measured in order to compare different algorithms.
Daniel Pucher, Walter G. Kropatsch
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004
Edit distance was originally developed by Levenstein several decades ago to measure the distance between two strings. It was found that this distance can be computed by an elegant dynamic programming procedure. The edit distance has played important roles in a wide array of applications due to its representational efficacy and computational efficiency.
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Edit distance was originally developed by Levenstein several decades ago to measure the distance between two strings. It was found that this distance can be computed by an elegant dynamic programming procedure. The edit distance has played important roles in a wide array of applications due to its representational efficacy and computational efficiency.
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Swap and mismatch edit distance
Algorithmica, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Amir, Amihood +2 more
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