Pattern selection for testing of deep sub-micron timing defects

2004 ◽  
Author(s):  
Mango ◽  
C.-T. Chao ◽  
L.-C. Wang ◽  
Kwang-Ting Cheng
Keyword(s):  
Pattern Selection ◽  
Selection For ◽  
Timing Defects

scholarly journals Automatic Gait Pattern Selection for Legged Robots

2020 ◽  
Author(s):  
Jiayi Wang ◽  
Iordanis Chatzinikolaidis ◽  
Carlos Mastalli ◽  
Wouter Wolfslag ◽  
Guiyang Xin ◽  
...  
Keyword(s):  
Gait Pattern ◽  
Legged Robots ◽  
Pattern Selection ◽  
Selection For

Test Pattern Selection for Defect-Aware Test

2011 ◽  
Author(s):  
Yoshinobu Higami ◽  
Hiroshi Furutani ◽  
Takao Sakai ◽  
Shuichi Kameyama ◽  
Hiroshi Takahashi
Keyword(s):  
Test Pattern ◽  
Pattern Selection ◽  
Selection For ◽  
Test Pattern Selection

scholarly journals On pattern selection for laparoscope calibration

2017 ◽  
Author(s):  
Stephen Thompson ◽  
Yannic Meuer ◽  
Eddie Edwards ◽  
João Ramalhinho ◽  
Maria Ruxandra Robu ◽  
...  
Keyword(s):  
Pattern Selection ◽  
Selection For

Neighborhood Property–Based Pattern Selection for Support Vector Machines

2007 ◽  
Vol 19 (3) ◽  
pp. 816-855 ◽  
Author(s):  
Hyunjung Shin ◽  
Sungzoon Cho
Keyword(s):  
Learning Community ◽  
Large Data ◽  
Support Vector ◽  
Pattern Selection ◽  
Data Set ◽  
Vector Machines ◽  
Large Memory ◽  
Long Time ◽  
Practical Performance ◽  
Selection For

The support vector machine (SVM) has been spotlighted in the machine learning community because of its theoretical soundness and practical performance. When applied to a large data set, however, it requires a large memory and a long time for training. To cope with the practical difficulty, we propose a pattern selection algorithm based on neighborhood properties. The idea is to select only the patterns that are likely to be located near the decision boundary. Those patterns are expected to be more informative than the randomly selected patterns. The experimental results provide promising evidence that it is possible to successfully employ the proposed algorithm ahead of SVM training.

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