scholarly journals Training Set Design for Test Removal Classication in IC Test

2000 ◽  
Author(s):  
Nagarjun Hassan Ranganath
Keyword(s):  
Set Design ◽  
Design For Test ◽  
Training Set ◽  
Ic Test

scholarly journals Neural network-based software sensor: training set design and application to a continuous pulp digester

2005 ◽  
Vol 13 (2) ◽  
pp. 135-143 ◽  
Author(s):  
Pascal Dufour ◽  
Sharad Bhartiya ◽  
Prasad S. Dhurjati ◽  
Francis J. Doyle III
Keyword(s):  
Neural Network ◽  
Set Design ◽  
Training Set ◽  
Software Sensor ◽  
Pulp Digester ◽  
Design And Application

Variable selection and training set design for particle classification using a linear and a non-linear classifier

2017 ◽  
Vol 173 ◽  
pp. 131-144 ◽  
Author(s):  
Stefan Heisel ◽  
Tijana Kovačević ◽  
Heiko Briesen ◽  
Gerhard Schembecker ◽  
Kerstin Wohlgemuth
Keyword(s):  
Variable Selection ◽  
Set Design ◽  
Training Set ◽  
Linear Classifier ◽  
Non Linear ◽  
And Training ◽  
Particle Classification

scholarly journals Training set design in genomic prediction with multiple biparental families

2021 ◽  
Author(s):  
Xintian Zhu ◽  
Willmar L. Leiser ◽  
Volker Hahn ◽  
Tobias Würschum
Keyword(s):  
Genomic Prediction ◽  
Set Design ◽  
Training Set

scholarly journals Testing peatland testate amoeba transfer functions: Appropriate methods for clustered training-sets

The Holocene ◽  
2011 ◽  
Vol 22 (7) ◽  
pp. 819-825 ◽  
Author(s):  
Richard J Payne ◽  
Richard J Telford ◽  
Jeffrey J Blackford ◽  
Antony Blundell ◽  
Robert K Booth ◽  
...  
Keyword(s):  
Cross Validation ◽  
Transfer Functions ◽  
Model Performance ◽  
Set Design ◽  
Testate Amoeba ◽  
Data Sets ◽  
Training Set ◽  
Traditional Training ◽  
Performance Statistics ◽  
Training Sets

Transfer functions are widely used in palaeoecology to infer past environmental conditions from fossil remains of many groups of organisms. In contrast to traditional training-set design with one observation per site, some training-sets, including those for peatland testate amoeba-hydrology transfer functions, have a clustered structure with many observations from each site. Here we show that this clustered design causes standard performance statistics to be overly optimistic. Model performance when applied to independent data sets is considerably weaker than suggested by statistical cross-validation. We discuss the reasons for these problems and describe leave-one-site-out cross-validation and the cluster bootstrap as appropriate methods for clustered training-sets. Using these methods we show that the performance of most testate amoeba-hydrology transfer functions is worse than previously assumed and reconstructions are more uncertain.

Impact of Training Set Design in CNN-Based Sar Image Despeckling

2021 ◽  
Author(s):  
A. Mazza ◽  
G. Scarpa ◽  
L. Verdoliva ◽  
G. Poggi
Keyword(s):  
Set Design ◽  
Sar Image ◽  
Training Set ◽  
Image Despeckling
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