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What is PAC Learning: Data Requirements & Accuracy about?

Probably Approximately Correct (PAC) Learning is a framework that analyzes the data requirements for learning algorithms to perform well on unseen data with high probability and low error. A concept class is PAC-learnable if there exists an algorithm that outputs a hypothesis with low error given sufficient labeled examples. The goal is to develop algorithms that can learn any function from a hypothesis space effectively, as illustrated by the example of determining if a person is medium built based on heig

Author

ihtgoot

Language

EN