Logistic regression for crystal growth process modeling through hierarchical nonnegative garrote-based variable selection-Reference-Cited by-同舟云学术

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Logistic regression for crystal growth process modeling through hierarchical nonnegative garrote-based variable selection

Published:2016-06-09 Issue:8 Volume:48 Page:787-796
ISSN:0740-817X
Container-title:IIE Transactions
language:en
Short-container-title:IIE Transactions

Author:

Sun Hongyue¹,Deng Xinwei²,Wang Kaibo³,Jin Ran¹

Affiliation:

1. Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA

2. Department of Statistics, Virginia Tech, Blacksburg, VA, USA

3. Department of Industrial Engineering, Tsinghua University, Beijing, People's Republic of China

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/0740817X.2016.1167286

Reference48 articles.

1. A new look at the statistical model identification

2. Neural network reinforced point defect concentration estimation model for Czochralski-grown silicon crystals

3. The use of the area under the ROC curve in the evaluation of machine learning algorithms

4. Better Subset Regression Using the Nonnegative Garrote

5. Engineering analysis of microdefect formation during silicon crystal growth

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