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2017 | OriginalPaper | Chapter

2. Probabilities: Bayesian Classifiers

Author: Miroslav Kubat

Publisher: Springer International Publishing

Published in: An Introduction to Machine Learning

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Abstract

The earliest attempts to predict an example’s class based on the known attribute values go back to well before World War II—prehistory, by the standards of computer science. Of course, nobody used the term “machine learning,” in those days, but the goal was essentially the same as the one addressed in this book.

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We assume here that 100 is the maximum value observed in the training set. Alternatively, our background knowledge may inform us that the given attribute’s value cannot exceed 100.

14.

Chow, C. K. (1957). An optimum character recognition system using decision functions. IRE Transactions on Computers, EC-6, 247–254. MathSciNetCrossRef

25.

Fisher, R. A. (1936). The use of multiple measurement in taxonomic problems. Annals of Eugenics, 7, 111–132.

33.

Good, I. J. (1965). The estimation of probabilities: An essay on modern Bayesian methods. Cambridge: MIT. MATH

72.

Neyman, J. & Pearson E. S. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference. Biometrica, 20A, 175–240. MATH

74.

Parzen E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33, 1065–1076. MathSciNetCrossRefMATH

Title

Probabilities: Bayesian Classifiers

Book

An Introduction to Machine Learning

Print ISBN: 978-3-319-63912-3

Electronic ISBN: 978-3-319-63913-0

https://doi.org/10.1007/978-3-319-63913-0

DOI

https://doi.org/10.1007/978-3-319-63913-0_2

Author:

Miroslav Kubat

Publisher

Springer International Publishing

Sequence number

Chapter number

Chapter 2