Linear predictor function

In statistics and in machine learning, a linear predictor function is a linear function (linear combination) of a set of coefficients and explanatory variables (independent variables), whose value is used to predict the outcome of a dependent variable.[1] This sort of function usually comes in linear regression, where the coefficients are called regression coefficients. However, they also occur in various types of linear classifiers (e.g. logistic regression,[2] perceptrons,[3] support vector machines,[4] and linear discriminant analysis[5]), as well as in various other models, such as principal component analysis[6] and factor analysis. In many of these models, the coefficients are referred to as "weights".

  1. ^ Makhoul, J. (1975). "Linear prediction: A tutorial review". Proceedings of the IEEE. 63 (4): 561–580. Bibcode:1975IEEEP..63..561M. doi:10.1109/PROC.1975.9792. ISSN 0018-9219.
  2. ^ David A. Freedman (2009). Statistical Models: Theory and Practice. Cambridge University Press. p. 26. ISBN 9780521743853. A simple regression equation has on the right hand side an intercept and an explanatory variable with a slope coefficient. A multiple regression equation has two or more explanatory variables on the right hand side, each with its own slope coefficient
  3. ^ Rosenblatt, Frank (1957), The Perceptron--a perceiving and recognizing automaton. Report 85-460-1, Cornell Aeronautical Laboratory.
  4. ^ Cortes, Corinna; Vapnik, Vladimir N. (1995). "Support-vector networks" (PDF). Machine Learning. 20 (3): 273–297. CiteSeerX 10.1.1.15.9362. doi:10.1007/BF00994018.
  5. ^ McLachlan, G. J. (2004). Discriminant Analysis and Statistical Pattern Recognition. Wiley Interscience. ISBN 978-0-471-69115-0. MR 1190469.
  6. ^ Jolliffe I.T. Principal Component Analysis, Series: Springer Series in Statistics, 2nd ed., Springer, NY, 2002, XXIX, 487 p. 28 illus. ISBN 978-0-387-95442-4