ML Theory Lecture
نویسنده
چکیده
Coherence. In this problem we are considering a curious setup where past and future learning are not distinct; instead, vectors x ∈ Rd just keep coming, along with correct labels y ∈ {−1,+1}, and we can learn forever if we wish. Coherence will be provided in the following interesting way. There will be a fixed vector u ∈ Rd and scalar γ > 0 (“fixed” means: fixed across all time) so that every pair (x , y) we receive satisfies y sgn(〈w , x〉) and | 〈u , x〉 | ≥ γ. More simply, these two cases can be combined into one: every pair (x , y) satisfies 〈 w , x y 〉 ≥ γ. [ In class: a picture was drawn. ]
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