SBSS.P: 'DSAM' - SBSS.P algorithm
In DSAM: Data Splitting Algorithms for Model Developments
SBSS.P R Documentation
'DSAM' - SBSS.P algorithm
Description
SBSS.P algorithm is a stochastic algorithm. It obtains data subsets through uniform sampling in each neuron after clustering through SOM neural network, with details given in May et al. (2010).
Usage
SBSS.P(data, control)
Arguments
data
The dataset should be matrix or Data.frame. The format should be as follows: Column one is a subscript vector used to mark each data point (each row is considered as a data point); Columns from 2 to N-1 are the input data, and Column N are the output data.
control
User-defined parameter list, where each parameter definition refers to the par.default function.
Value
Return the training, test and validation subsets. If the original data are required to be split into two subsets, the training and test subsets can be combined into a single calibration subset.
References
May, R. J., Maier H. R., and Dandy G. C.(2010), Data splitting for artificial neural networks using SOM-based stratified sampling, Neural Netw, 23(2), 283-294.
DSAM documentation built on May 29, 2024, 3:05 a.m.
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| SBSS.P | R Documentation |
'DSAM' - SBSS.P algorithm
Description
SBSS.P algorithm is a stochastic algorithm. It obtains data subsets through uniform sampling in each neuron after clustering through SOM neural network, with details given in May et al. (2010).
Usage
SBSS.P(data, control)
Arguments
data |
The dataset should be matrix or Data.frame. The format should be as follows: Column one is a subscript vector used to mark each data point (each row is considered as a data point); Columns from 2 to N-1 are the input data, and Column N are the output data. |
control |
User-defined parameter list, where each parameter definition refers to the par.default function. |
Value
Return the training, test and validation subsets. If the original data are required to be split into two subsets, the training and test subsets can be combined into a single calibration subset.
References
May, R. J., Maier H. R., and Dandy G. C.(2010), Data splitting for artificial neural networks using SOM-based stratified sampling, Neural Netw, 23(2), 283-294.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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