What is the difference between computational learning theory and machine learning?
计算学习理论和机器学习之间的区别是什么？
As the name suggests, computational learning theory seems to be the study of computer systems that learn.
顾名思义，计算学习理论似乎是计算机系统的学习研究。
So I'd say machine learning is guided and inspired by the theoretical results you get from computational learning theory.
所以我想说，机器学习是引导和启发你从计算学习理论的理论结果。
I rather think it's talking of computational learning theory as a subfield of mathematics, though I admit that it can be misleading.
我想这是在计算学习理论作为数学的一个分支，虽然我承认这会误导。
However, according to the computational learning theory article, computational learning theory is just a field that is related to machine learning.
然而，根据计算学习理论第，计算学习理论是一个领域，是机器学习相关。
PAC learning model is the fundamental of computational learning theory, it provides a probabilistic framework for the study of learning and generalization.
而PAC学习模型是计算学习理论的基础，它为研究学习及泛化问题提供了一种基本的概率框架。
I'm seeing that the machine learning article talks about computational learning theory as a "subfield", maybe you got the wrong impression that the word "subfield" is referred to machine learning.
我看到了机器学习的文章讨论了计算学习理论作为一种“分支”，也许你有，“子”是指机器学习错误的印象。
Computational learning theory provides formal framework for comparing the performance of two algorithms, and can determine the sample complexity and the computational complexity of some concept class.
计算学习理论为比较两算法的性能提供了形式化的框架，并能确定某概念类的计算复杂度和样本复杂度。
A computational theory of learning from examples, extension matrix theory, is presented.
本文提出示例学习的一种计算理论，扩张矩阵论。
A computational theory of learning from examples, extension matrix theory, is presented.
本文提出示例学习的一种计算理论，扩张矩阵论。
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