This paper demonstrates that PAC learning can also be used to analyzesemantic bias, such as a domain theory about the concept being learned. The key idea is to ...
Abstract. Prior knowledge, or bias, regarding a concept can reduce the number of examples needed to learn it. Probably Approximately Correct (PAC) learning ...
The key idea is to view the hypothesis space in PAC learning as that consistent with all prior knowledge, syntactic and semantic. In particular, the paper ...
This paper demonstrates that PAC learning can also be used to analyze semantic bias, such as a domain theory about the concept being learned, and presents ...
The key idea is to view the hypothesis space in PAC learning as that consistent with all prior knowledge, syntactic and semantic. In particular, the paper ...
Abstract This article summarizes work on developing a learning theory account for the major learning and statistics based approaches used in natural language ...
May 20, 2024 · PAC learning aims to determine whether a learning algorithm can, with high probability, produce a hypothesis that is approximately correct. This ...
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Prior knowledge, or bias, regarding a concept can reduce the number of examples needed to learn it. Probably Approximately Correct (PAC) learning is a ...
... decision problems using average reward reinforcement learning ... Quantifying Prior Determination Knowledge Using the PAC Learning Model Machine Learning.
