Version du 12 avril 2019 à 11:02

Neural networks

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
Data analysis
Validation of results

@@ Ligne 7 : / Ligne 7 : @@
 * Outlier detection and removal
 * Data normalization, one of:
-** Min-Max Normalization, min-max normalization is a linear scaling algorithm. It transforms the original input range into a new data range (typically 0-1).
+** Min-Max Normalization: linear scaling into a data range, typically [0,1]
-** Zscore Normalization, in Zscore normalization, the 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, sigmoidal normalization is a nonlinear transformation. It transforms the input data into the range -1 to 1, using a sigmoid function
+** Sigmoidal Normalization: nonlinear transformation of the input data into the range [-1,1], with a sigmoid function
 * Data analysis
 * Validation of results