Generalized reduced error logistic regression method

Details Generalized reduced error logistic regression method Generalized reduced error logistic regression method

2008-07-31

2011-10-04

Vincent, David R (Department: 2129)

Data processing: artificial intelligence

Knowledge processing system

Knowledge representation and reasoning technique

Reexamination Certificate

active

08032473

ABSTRACT:
A machine classification learning method titled Generalized Reduced Error Logistic Regression (RELR) is presented. The method overcomes significant limitations in prior art logistic regression and other machine classification learning methods. The method is applicable to all current applications of logistic regression, but has significantly greater accuracy using smaller sample sizes and larger numbers of input variables than other machine classification learning methods including prior art logistic regression.

REFERENCES:
patent: 5825907 (1998-10-01), Russo
Ana M. Aguilera, Manuel Escabias, and Mariano J. Valderrama, “Using principal components for estimating logistic regression with high-dimensional multicollinear data”, Computational Statistics 7 Data Analysis 50 (2006) 1905-24.
K. Mehrotra, C. Mohan, and S. Ranka, “Elements of Artificial Neural Networks”, MIT Press 2000, pp. 20, 25, and 33.

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