M. Schumacher, R. Robner and W. Vach, Neural networks and logistic regression: Part I, Computational Statistics & Data Analysis 21 (1996) 661--682. Home/Search Document Details and Download
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An Investigation of Neural Networks in Thyroid Function Diagnosis - Zhang, Berardi (1998) (Correct)
....neural network jargon into statistical terminology and illustrates the relationship between neural networks and statistical models such as generalized linear models, projection pursuit and cluster analysis. Warner and Misra [30] contrast neural networks to regression models. Schumacher et al. [25] and Vach et al. 29] present a thorough comparison between feedforward neural networks and the logistic regression. The conceptual similarities and discrepancies between the two methods are analyzed. Gallinari et al. 6] study the relations between discriminant analysis and multilayer perceptrons ....
M. Schumacher, R. Robner and W. Vach, Neural networks and logistic regression: Part I, Computational Statistics & Data Analysis 21 (1996) 661--682.
About the Analysis of Septic Shock Patient Data - Paetz, Hamker, Thöne (2000) (1 citation) (Correct)
....(see [3] and [4] Concerning our problem supervised neural networks have the following positive aspects: nonlinear classi cation, fault tolerance, learning from data and generalization ability. The aim of this contribution is not a comparison of statistical with neural network methods (see [5]) but to select an appropriate method that can easily be adapted to our data. Here, our aim is to detect critical illness states with a classi cation method. Linear classi ers did not seem to be suitable for a classi cation after having a look at the data. In addition, a nonlinear method surely ....
Schumacher, M., Roner, R., Vach, W.: Neural Networks and Logistic Regression: Part I. Computational Statistics & Data Analysis 21 (1996) 661-682
Visualization and Implementation of Feedforward Neural Networks - Klinke, Grassmann (1996) (1 citation) (Correct)
....exactly one of the given classes. To reach the outputs sum to one the softmax method (Bridle 1990) can be chosen, that is to model f k (x) P (y k = 1jx) exp(w T x) P K j=1 exp(w T x) Both approaches should yield the same classification results as the polytomous logistic regression (Schumacher, Rossner Vach 1996, Vach, Rossner Schumacher 1996) In each case the class with maximum corresponding output is chosen as derived from the Bayesian decision rule. While the FFN models were fitted in S plus using the nnet library, the visualization was done in XploRe using the nn macro. In figure 19 one can see ....
.... sum to one the softmax method (Bridle 1990) can be chosen, that is to model f k (x) P (y k = 1jx) exp(w T x) P K j=1 exp(w T x) Both approaches should yield the same classification results as the polytomous logistic regression (Schumacher, Rossner Vach 1996, Vach, Rossner Schumacher 1996). In each case the class with maximum corresponding output is chosen as derived from the Bayesian decision rule. While the FFN models were fitted in S plus using the nnet library, the visualization was done in XploRe using the nn macro. In figure 19 one can see the visualized version of the ....
Schumacher, M., Rossner, R. & Vach, W. (1996). Neural networks and logistic regression : Part i, Computational Statistics & Data Analysis 21(6): 661-- 682.
On the Misuses of Artificial Neural Networks for.. - Schwarzer, Vach.. (2000) (1 citation) Self-citation (Schumacher Vach) (Correct)
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Schumacher M, Roßner R, Vach W. Neural networks and logistic regression: Part I. Computational Statistics and Data Analysis. 1996;21:661-82.
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