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...
Probably approximately correct learning, PAC-learning, is a framework for the study of learnability and learning machines. In this framework, learning is i... A Hernandez-Aguirre,BP Buckles,A Martinez-Alcántara - IEEE 被引量: 24发表: 2000年 Probably Approximately Correct MDP Learning and Control...
In this article, PAC-learning theory is applied to model inference, which concerns the problem of inferring theories from facts in first order logic. It is argued that uniform sample PAC-learnabilityDOI: 10.1007/3-540-57868-4_60 被引量: 4 年份...
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 ...
PAC learnability and VC dimension are closely related: H is agnostically PAC-learnable if and only if H has a finite VC dimension. In this case, we can calculate the sample complexity using the VC dimension of the hypothesis space : where is the learner’s maximum error with the ...
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....
In many cases, machine learning seems to work seamlessly, but is there any way to formally determine the learnability of a concept? In 1984, the computer scientist L. Valiant proposed a mathematical approach to determine whether a problem is learnable by a computer. The name of this technique...
3. PAC-learnability of Probabilistic Deterministic Finite State Automata [J] . Clark Alexander, Thollard Franck Journal of machine learning research . 2004,第May期 机译:概率确定性有限状态自动机的PAC可学习性 4. Towards Feasible PAC-Learning of Probabilistic Deterministic Finite A...
Learnability with respect to fixed distributions Theoretical Computer Science, 86 (2) (1991), pp. 377-389 Google Scholar [7] Avrim Blum, Tom Mitchell, Combining labeled and unlabeled data with co-training, in: Proceedings of the 11th Annual Conference on Computational Learning Theory, 1998, pp...