Inductive Inference Theory - A Unified Approach to Problems in Pattern Recognition and Artificial Intelligence
Proceedings of the Fourth International Joint Conference on Artificial Intelligence. Learning Debugging And Automatic Programming. Pages 274-279.
Abstract
Recent results in in:iuction theory are reviewed that demonstrate the general adequacy of the induction system of Solomoncff ar.d Willis. Several problems in pattern recognition and A.I. are investigated through these methods. The theory is used to obtain the a priori probabilities that are necessary in the application cf st.ochastic languages to pattern recognition. A simple, quantitative solution is presentee for part of Winston's problem of learning structural descriptions from examples. In c ont.ra~t to work :l n non-prcbabi .listic prediction, the present methods give probability values that can be usee with decision theory to make critical decisions.
@inproceedings{ijcai1975p42,
title = {Inductive Inference Theory - A Unified Approach to Problems in Pattern Recognition and Artificial Intelligence},
author = {Solomonott, R.},
booktitle = {Proceedings of the Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-75}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Patrick Winston and Carl Hewitt},
pages = {274--279},
year = {1975},
note = {Learning Debugging And Automatic Programming},
url = {https://ijcai.org/proceedings/1975/42},
}
TY - CPAPER TI - Inductive Inference Theory - A Unified Approach to Problems in Pattern Recognition and Artificial Intelligence AU - Solomonott, R. T2 - Proceedings of the Fourth International Joint Conference on Artificial Intelligence, IJCAI-75 PB - International Joint Conferences on Artificial Intelligence PY - 1975 SP - 274 EP - 279 UR - https://ijcai.org/proceedings/1975/42 ER -Copied
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