scholarly journals Phonemic similarity metrics to compare pronunciation methods

2011 ◽  
Author(s):  
Ben Hixon ◽  
Eric Schneider ◽  
Susan L. Epstein
Keyword(s):  
Similarity Metrics ◽  
Phonemic Similarity

USE OF SIMILARITY METRICS IN TEMPLATE-BASED DETECTION OF OBJECTS IN IMAGES

2019 ◽  
Vol 78 (14) ◽  
pp. 1249-1261
Author(s):  
O. Rubel ◽  
S. K. Abramov ◽  
V. V. Abramova ◽  
V. V. Lukin
Keyword(s):  
Similarity Metrics ◽  
Detection Of Objects

scholarly journals Phylogenetic relations and mitogenome‐wide similarity metrics reveal monophyly of Penaeus sensu lato

2021 ◽  
Vol 11 (5) ◽  
pp. 2040-2049
Author(s):  
Vinaya Kumar Katneni ◽  
Mudagandur S. Shekhar ◽  
Ashok Kumar Jangam ◽  
Balasubramanian C. Paran ◽  
Ashok Selvaraj ◽  
...  
Keyword(s):  
Similarity Metrics ◽  
Phylogenetic Relations

Methods for detecting and correcting contextual data quality problems

2021 ◽  
Vol 25 (4) ◽  
pp. 763-787
Author(s):  
Alladoumbaye Ngueilbaye ◽  
Hongzhi Wang ◽  
Daouda Ahmat Mahamat ◽  
Ibrahim A. Elgendy ◽  
Sahalu B. Junaidu
Keyword(s):  
Data Quality ◽  
Quality Evaluation ◽  
Web Applications ◽  
Support Vector ◽  
Similarity Metrics ◽  
Distributed Data ◽  
Contextual Data ◽  
Formal Taxonomy ◽  
E Learning ◽  
High Scalability

Knowledge extraction, data mining, e-learning or web applications platforms use heterogeneous and distributed data. The proliferation of these multifaceted platforms faces many challenges such as high scalability, the coexistence of complex similarity metrics, and the requirement of data quality evaluation. In this study, an extended complete formal taxonomy and some algorithms that utilize in achieving the detection and correction of contextual data quality anomalies were developed and implemented on structured data. Our methods were effective in detecting and correcting more data anomalies than existing taxonomy techniques, and also highlighted the demerit of Support Vector Machine (SVM). These proposed techniques, therefore, will be of relevance in detection and correction of errors in large contextual data (Big data).

scholarly journals The VC Dimension of Metric Balls under Fréchet and Hausdorff Distances

2021 ◽  
Author(s):  
Anne Driemel ◽  
André Nusser ◽  
Jeff M. Phillips ◽  
Ioannis Psarros
Keyword(s):  
Density Estimation ◽  
Lower Bounds ◽  
Upper And Lower Bounds ◽  
Similarity Metrics ◽  
Vc Dimension ◽  
Set Systems ◽  
Polygonal Curves ◽  
The Individual ◽  
Curve Similarity ◽  
Metric Balls

AbstractThe Vapnik–Chervonenkis dimension provides a notion of complexity for systems of sets. If the VC dimension is small, then knowing this can drastically simplify fundamental computational tasks such as classification, range counting, and density estimation through the use of sampling bounds. We analyze set systems where the ground set X is a set of polygonal curves in $$\mathbb {R}^d$$ R d and the sets $$\mathcal {R}$$ R are metric balls defined by curve similarity metrics, such as the Fréchet distance and the Hausdorff distance, as well as their discrete counterparts. We derive upper and lower bounds on the VC dimension that imply useful sampling bounds in the setting that the number of curves is large, but the complexity of the individual curves is small. Our upper and lower bounds are either near-quadratic or near-linear in the complexity of the curves that define the ranges and they are logarithmic in the complexity of the curves that define the ground set.

Meaningfulness, phonemic similarity, and sensory memory.

1973 ◽  
Vol 98 (1) ◽  
pp. 64-69
Author(s):  
Margaret J. Peterson ◽  
Carol E. Eger ◽  
Gregory G. Brown
Keyword(s):  
Sensory Memory ◽  
Phonemic Similarity

scholarly journals A new subjective procedure for evaluation and development of texture similarity metrics

2011 ◽  
Author(s):  
Jana Zujovic ◽  
Thrasyvoulos N. Pappas ◽  
David L. Neuhoff ◽  
Rene van Egmond ◽  
Huib de Ridder
Keyword(s):  
Similarity Metrics

MuSim: Mutation-based Fault Localization Using Test Case Proximity

2021 ◽  
Vol 31 (05) ◽  
pp. 725-744
Author(s):  
Arpita Dutta ◽  
Amit Jha ◽  
Rajib Mall
Keyword(s):  
Fault Localization ◽  
Test Case ◽  
Test Cases ◽  
Similarity Metrics ◽  
Localization Technique ◽  
Evaluation Metric

Fault localization techniques aim to localize faulty statements using the information gathered from both passed and failed test cases. We present a mutation-based fault localization technique called MuSim. MuSim identifies the faulty statement based on its computed proximity to different mutants. We study the performance of MuSim by using four different similarity metrics. To satisfactorily measure the effectiveness of our proposed approach, we present a new evaluation metric called Mut_Score. Based on this metric, on an average, MuSim is 33.21% more effective than existing fault localization techniques such as DStar, Tarantula, Crosstab, Ochiai.

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