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** Zscore normalization: input variable data is converted into zero mean and unit variance | ** Zscore normalization: input variable data is converted into zero mean and unit variance | ||
** Sigmoidal normalization: nonlinear transformation of the input data into the range [-1,1], with a sigmoid function | ** Sigmoidal normalization: nonlinear transformation of the input data into the range [-1,1], with a sigmoid function | ||
| + | ** Other normalization: e.g. if a variable is exponentially distributed, take the logarithm | ||
* Data analysis | * Data analysis | ||
* Validation of results | * Validation of results | ||
Version du 12 avril 2019 à 10:10
Neural networks
Pre-processing
- Data exploration (clean-up)
- Data transformation
- Outlier detection and removal
- Data normalization, one of:
- Min-Max normalization: linear scaling into a data range, typically [0,1]
- Zscore normalization: input variable data is converted into zero mean and unit variance
- Sigmoidal normalization: nonlinear transformation of the input data into the range [-1,1], with a sigmoid function
- Other normalization: e.g. if a variable is exponentially distributed, take the logarithm
- Data analysis
- Validation of results