•  
  •  
 

PYTHAGORAS : Jurnal Matematika dan Pendidikan Matematika

Document Type

Article

Abstract

Logistic regression can be mapped equivalent to artificial neural network (ANN) without hidden layer with logistic as activation function, hence logistic regression is subset of ANN. The result study on binary and poly-chotomous response data show that parameter estimation values of ANN and logistic regression are similar. In comparison with ANN, logistic regression has standard procedure for estimation and testing parameter.
Keyword : logistic regression, generalized linear model, artificial neural network, activation function, hidden layer

Page Range

51-62

Issue

1

Volume

3

Digital Object Identifier (DOI)

10.21831/pg.v3i1.642

Source

https://journal.uny.ac.id/index.php/pythagoras/article/view/642

References

Cheng, B. & Titterington, D.M. 1994. Neural Network : A review from a Statistical Prespective. Statistical science 9 : 2-54.

Hosmer, D.W. & Lemeshow, S. (1989). Applied Logistic Regression. New York : John Wiley and Sons.

Jordan, M.I. & Bishop, C.M. (1996). Neural Network. AI Memo No. 152, C,B.C.L. Memo No 131. Massachusetts Institute of Technology.

McCullagh, P. & Nelder, J.A. (1989). Generalized Linear Models. New York: Chapman and Hall.

Sarle, W.S. (1994). Neural Network and Statistical Models. Proceedings of the 19th Anual SAS User Group International Conference.

Sarle, W.S. (1996). Neural Network and Statistical Jargon. Diambil tanggal 16 Juli 2007 dari ftp://ftp.sas.com/pub/neural/jargon.

Schumacher, M., Robner R., & Vach W. (1996). Neural Network and logistic regression : Part I. J. Computational Statistics and Data Analysis 21:661-682.

Warner, B. & Misra, M. (1996). Understanding Neural Network as Statistical Tools. American Statistician 50 : 284-293

Download

Included in

Mathematics Commons

Share

COinS