RProp MLP Learner0 ×
DeprecatedKNIME Base Nodes version 3.6.0.v201807061308 by KNIME AG, Zurich, Switzerland
Implementation of the RProp algorithm for multilayer feedforward networks.
RPROP performs a local adaptation of the weight-updates according to the
behavior of the error function.
For further details see: Riedmiller, M. Braun, H. : "A direct adaptive method for faster
backpropagation learning: theRPROP algorithm",Proceedings of the IEEE
International Conference on Neural Networks (ICNN) (Vol. 16, pp. 586-591).
Piscataway, NJ: IEEE.
This node provides a view of the error plot.
If the optional PMML inport is connected and contains
preprocessing operations in the TransformationDictionary those are
added to the learned model.
Options
- Maximum number of iterations
- The number of learning iterations.
- Number of hidden layers
- Specifies the number of hidden layers in the architecture of the neural network.
- Number of hidden neurons per layer
- Specifies the number of neurons contained in each hidden layer.
- Class column
- Choose the column that contains the target variable: it can either be nominal or numerical. All nominal class values are extracted and assigned to output neurons. If you use a numerical target variable (regression), please make sure it is normalized!
- Ignore missing values
- If this checkbox is set, rows with missing values will not be used for training.
- Use seed for random initialization
- If this checkbox is set, a seed (see next field) can be set for initializing the weights and thresholds can be set.
- Random seed
- Seed for the random number generator.
Input Ports
Output Ports
Views
- Error Plot
- Displays the error for each iteration.
Workflows
- 03_ZIP_Local_vs_remoteHTTP
- 02_Techniques_for_Dimensionality_Reduction
- 01_Analytics
- 002005_PMML_Examples
- Dim Reduction Techniques
- KDD analysis on All Data
- 050002_CreditScoring
- 060001_PMML_webinar
- Show all 8 workflows
Update Site
To use this node in KNIME, install KNIME Base Nodes from the following update site: