INDUCTIVE INFERENCE THEORY - A UNIFIED APPROACH TC FROEIEMS IN PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE

机译：归纳推理理论 - 一种统一的方法识别和人工智能中的统一方法

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Recent results in induction theory are reviewed that demonstrate the general adequacy of the induction system of Solomoncff and Willis. Several problems inpattern 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 stochastic languages to pattern recognition. A simple, quantitative solution is presented for part of Winston's problem of learning structural descriptions from examples. In contrast to work in non - probabilistic prediction, the present methods give probability values t h a t can be used with decision. theory to make critical decisions.

机译：综述了近期导致归纳理论的结果，证明了所罗门科和威利斯的归纳系统的一般充分性。无论如何的若干问题和A.I.通过这些方法调查。该理论用于获得应用CF随机语言所需的先验概率，以模式识别。呈现简单的定量解决方案，是Winston从例子学习结构描述的问题的一部分。与非概率预测的工作相比，本方法给出概率值T H A T可以与决定一起使用。理论做出关键决策。

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《International Joint Conference on Artificial Intelligence》|2007年||共7页
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R. J. Solomoncff;
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中图分类 TP18-53;
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INDUCTIVE INFERENCE THEORY - A UNIFIED APPROACH TC FROEIEMS IN PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE

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