dnn: Fit a deep learning model via keras.

In statcodes/RaSE: Random Subspace Ensemble Classification and Variable Screening

Fit a deep learning model via keras.

Description

Fit a deep learning model.

Usage

dnn(
  x_train,
  y_train,
  levels = 4,
  max_units = 256,
  dropout_rate = 0.1,
  start = 0
)

Arguments

x_train	a
y_train	a
levels	a
max_units	a
dropout_rate	a
start	a

Value

An Keras fitted model.

model

The fitted Keras fitted model.

References

Tian, Y. and Feng, Y., 2021(a). RaSE: A variable screening framework via random subspace ensembles. Journal of the American Statistical Association, (just-accepted), pp.1-30.

Tian, Y. and Feng, Y., 2021(b). RaSE: Random subspace ensemble classification. Journal of Machine Learning Research, 22(45), pp.1-93.

Zhu, J. and Feng, Y., 2021. Super RaSE: Super Random Subspace Ensemble Classification. https://www.preprints.org/manuscript/202110.0042

Chen, J. and Chen, Z., 2008. Extended Bayesian information criteria for model selection with large model spaces. Biometrika, 95(3), pp.759-771.

Chen, J. and Chen, Z., 2012. Extended BIC for small-n-large-P sparse GLM. Statistica Sinica, pp.555-574.

Akaike, H., 1973. Information theory and an extension of the maximum likelihood principle. In 2nd International Symposium on Information Theory, 1973 (pp. 267-281). Akademiai Kaido.

Schwarz, G., 1978. Estimating the dimension of a model. The annals of statistics, 6(2), pp.461-464.

statcodes/RaSE documentation built on April 21, 2024, 6:01 p.m.