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What is Sample Complexity in Learning Hypotheses about?

The document discusses sample complexity results in machine learning. It begins by introducing the distributional learning setting, where examples are drawn i.i.d. from a fixed distribution. The goal is to find a hypothesis with low error on the whole distribution based on a sample. It then presents a theorem showing that for a finite hypothesis space H, drawing a sample of size O(log(|H|)/ + log(1/δ)/) is sufficient to find a hypothesis with error at most with probability 1-δ. The document also discuss

Author

Chitradeep Dutta Roy

Language

EN