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Linear Support Vector Classifier (Linear SVC) - Python for Integrated Circuits - - An Online Book - |
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| Python for Integrated Circuits http://www.globalsino.com/ICs/ | ||||||||
| Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= The Linear Support Vector Classifier (SVC) method applies a linear kernel function to perform classification and performs well with a large number of samples. The Linear SVC has some parameters such as penalty normalization which applies 'L1' or 'L2' and loss function. In their keyword analysis by Kurian et al., [1] supervised machine learning was used with the Linear SVC to predict labels for incidents since it provides the highest accuracy.
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[1] Daniel Kurian, Fereshteh Sattari, Lianne Lefsrud, Yongsheng Ma, Using machine learning and keyword analysis to analyze incidents and reduce risk in oil sands operations, Safety Science, 130(2020), 104873.
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