A novel supervised learning approach based on ε-SVR and SSA: an example of hobbing parameters prediction.
Weidong CaoJianjun NiPublished in: ICCE-TW (2019)
Keyphrases
- supervised learning
- root mean square error
- optimal parameters
- prediction accuracy
- prediction model
- training data
- support vector regression
- maximum likelihood
- parameter estimation
- neural network
- training set
- sensitivity analysis
- prediction algorithm
- parameter values
- data sets
- learning tasks
- unsupervised learning
- semi supervised
- parameter settings
- expert systems
- input parameters
- support vector
- radial basis function neural network
- parameter optimization
- learning algorithm
- data mining
- extreme values
- linear regression model