Logistic Regression Realized with Artificial Neuron and Estimation Formulas

• Autorzy: Korohoda P. , Grabska-Chrząstowska J.
• Języki publikacji: EN

Abstrakty

EN

In the paper an experiment is described, that was designed and conducted to verify hypothesis that artificial neuron with sigmoidal activation function can efficiently solve the task of logistic regression in the case when the explaining variable is one-dimensional, and the explained variable is binomial. Computations were performed with 12 sets of statistical parameters, assumed for the generation of 65356 sets of data in each case. Comparative analysis of the obtained results with use of the reference values for the regression coefficients indicated that the investigated neuron can satisfactory perform the task, with efficiency similar to that obtained with classical logistic regression algorithm, when the teaching sets of input data, corresponding with output values 0 and 1, do not allow for simple separation. Moreover, it has been discovered that the simple formulas estimating the statistical distributions parameters from the samples, offer statistically superior assessment of the regression coefficient parameters.

Słowa kluczowe

EN

logistic regression analysis; comparison method; sigmoidal function

• Wydawca: Instytut Telekomunikacji i Informatyki Uniwersytetu Technologiczno-Przyrodniczego w Bydgoszczy
• Czasopismo: Image Processing & Communications
• Rocznik: 2012
• Tom: Vol. 17, no. 4
• Strony: 265--274
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Korohoda P.

• AGH University of Science and Technology, Chair of Electronics, Krakow, Poland

autor

Grabska-Chrząstowska J.

• AGH University of Science and Technology, Chair of Automatics and Biomedical Engineering, Krakow, Poland

Bibliografia

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