Intensional Sets, Probability, and Machine Learning

Authors

  • Mark A. Moore
  • Hong Zhang

DOI:

https://doi.org/10.67945/jfa.v11i.160

Keywords:

intensional logic, set theory, machine learning, probability theory

Abstract

Robert S. Hartman, in his book The Structure of Value (1967), makes a major advance in the understanding of intensional logic and set theory. What is perhaps less appreciated is the understanding of how probability is incorporated into his work. Hong Zhang and Mark Moore have utilized Hartman’s work in probability to enhance machine learning theory. In this paper we articulate Hartman’s theory and examine how his theory is important for modern machine learning.

References

Cortes, C. and Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273-297.

Hartman, R.S. (1967). The Structure of Value. Carbondale, IL. Southern Illinois University Press.

Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. New York: Springer-Verlag.

Vapnik, V.N. (1999) An overview of statistical learning theory. IEE Transactions on Neural Networks, Vol. 10, No. 5, pp. 988-999.

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Published

2018-07-01

How to Cite

Moore, M. A., & Zhang, H. (2018). Intensional Sets, Probability, and Machine Learning. Journal of Formal Axiology: Theory and Practice, 11, 49–64. https://doi.org/10.67945/jfa.v11i.160

Issue

Vol. 11 (2018)

Section

Articles

License

All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form without prior written permission from the Robert S. Hartman Institute, except as permitted by law.