Memory psychophysics and similarity comparison: Dember revisited.

2004 ◽  
pp. 335-357 ◽  
Author(s):  
William M. Petrusic ◽  
Peter H. Aubin
Keyword(s):  
Similarity Comparison

Defect Detection Technique Based on Similarity Comparison of Token

2013 ◽  
Vol 13 (20) ◽  
pp. 4270-4277
Author(s):  
Guo Tao ◽  
Dong Guowei ◽  
Qin Hu ◽  
Long Baolian ◽  
Qu Tong ◽  
...  
Keyword(s):  
Defect Detection ◽  
Detection Technique ◽  
Similarity Comparison

3D facial similarity comparison in shape space

2015 ◽  
Vol 23 (4) ◽  
pp. 1138-1145 ◽  
Author(s):  
赵俊莉 ZHAO Jun-li ◽  
武仲科 WU Zhong-ke ◽  
刘翠婷 LIU Cui-ting ◽  
段福庆 DUAN Fu-qing ◽  
周明全 ZHOU Ming-quan ◽  
...  
Keyword(s):  
Shape Space ◽  
Similarity Comparison ◽  
Facial Similarity

Influence of Non-Stationary Thermal Field on the Hardness of Welded Samples

2015 ◽  
Vol 811 ◽  
pp. 75-79
Author(s):  
Nicolae Grigore
Keyword(s):  
Thermal Field ◽  
Stationary Regime ◽  
Short Length ◽  
Similarity Comparison ◽  
Critical Areas ◽  
Hardness Values

The ends of the welding beads are the site of certain non-conformities due to the transitory regime used for welding. In these start and end areas of the welding beads, the thermal field is propagated in non-stationary regime, as opposed to the portions from the middle of the welding bead where the heat is propagated in quasistationary regime.This paper proposes to present the influence of non-stationary thermal field from the welding ends on the hardness values performed in such critical areas, by comparing these values with the values of hardness performed on the middle portions of welding beads. A similarity comparison is made with the situation of the welding works performed within the reconditioning process by welding, when the welding works are of short length executed with multiple rows, were the thermal field is also propagated in non-stationary regime.

lsemantica: A command for text similarity based on latent semantic analysis

2019 ◽  
Vol 19 (1) ◽  
pp. 129-142 ◽  
Author(s):  
Carlo Schwarz
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Learning Algorithm ◽  
Singular Value ◽  
Machine Learning Algorithm ◽  
Truncated Singular Value Decomposition ◽  
Similarity Comparison ◽  
Text Similarity ◽  
Semantic Relationships ◽  
Value Decomposition

In this article, I present the lsemantica command, which implements latent semantic analysis in Stata. Latent semantic analysis is a machine learning algorithm for word and text similarity comparison and uses truncated singular value decomposition to derive the hidden semantic relationships between words and texts. lsemantica provides a simple command for latent semantic analysis as well as complementary commands for text similarity comparison.

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