基于上面的设定,我们下面可以给出Agnostic PAC Learnability的定义 定义2.3 (Agnostic PAC Learnability)对于一个hypothesis class \mathcal H ,如果存在一个函数 m_\mathcal H :(0,1)^2\to \mathbb N 以及一个有如下性质的学习算法: 对于任意的 \epsilon,\delta \in (0,1) ,对于在 \mathcal X\times \...
PAC可学习是针对概念类谈的,而非特定的那个目标概念:Finally, the PAC framework deals with the question of learnability for a concept class \mathcal{C} and not a particular concept. Note that the concept class \mathcal{C} is known to the algorithm, but of course the target concept...
All concept classes considered are computable enumerations of computable \\({\\Pi^{0}_{1}}\\) classes, in a sense made precise here. This family of concept classes is sufficient to cover all standard examples, and also has the property that PAC learnability is equivalent to finite VC ...
A very important learning problem is the task oflearning a concept.Concept learninghas attracted much attention in learning theory. For having a running example, we look at humans who are able to distinguish between different “things,” e.g., chair, table, car, airplane, etc. There is no...
In fact, they conjecture a general negative result, which is nonetheless still absent. Show abstract Proper Learnability and the Role of Unlabeled Data 2025, Proceedings of Machine Learning Research Semi-supervised learning methods 2015, Jisuanji Xuebao/Chinese Journal of Computers Unlabeled data does ...
In other words, we prove that testing for PAC-learnability is undecidable in the Turing sense. We also briefly discuss some of the probable implications of this result to the current practice of machine learning. PDF Abstract Code Edit No code implementations yet. Submit your code now ...
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models machine-reasoning-ufrgs/mlbf • 13 Sep 2020 Computational learning theory states that many classes of boolean formulas are learnable in polynomial time....
Pac-learnability of probabilistic deterministic finite state automata. Journal of Machine Learning Research (JMLR), 5:473-497, 2004.A. Clark and F. Thollard, "Pac-Learnability of Probabilistic Deterministic Finite State Automata," J. Machine Learning Research, vol. 5, pp. 473-497, May 2004....
PAC Learnability of Node Functions in Networked Dynamical SystemsAbhijin AdigaChris J. KuhlmanMadhav MaratheS. S. RaviAnil VullikantiPMLRInternational Conference on Machine Learning
Probably approximately correct learning, PAC-learning, is a framework for the study of learnability and learning machines. In this framework, learning is induced through a set of examples. The size of this set is such that with probability greater than 1-/spl delta/ the learning machine shows ...