Lecturer : Rob Schapire Lecture # 9

نویسنده

  • Bryan Conroy
چکیده

However, suppose a learning algorithm cannot attain error rates less than some fixed amount, say 40%. Is it possible, despite this limitation, to still drive the error rate arbitrarily close to zero? This question motivates the weak learning model, which will be described below. This model will consider circumstances in which the learning algorithms can only do slightly better than random guessing, that is, error probability less than 50%. A concept class C is weakly learnable if ∃ γ > 0 ∃ algorithm A ∀ c ∈ C ∀ D ∀ δ > 0 A takes m = poly ( 1 δ , · · · ) examples to compute a hypothesis h such that

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تاریخ انتشار 2008