Theorem 28.3: for VCdim(H) = d, ε ∈ (0,1), δ ∈ (0,1/4) and m ≥ (8/ε)(2d log(16e/ε) + log(2/δ)), S ∼ D^m is an ε-net for H with probability ≥ 1 − δ
ProvedUnderstandingML.eps_net_theoremepsilon-netsample-complexitysymmetrizationvc-dimension
Theorem 28.3. Let with . Fix , and let
Then, with probability of at least over a choice of we have that is an -net for . consists of measurable hypotheses with the countable-approximation property of Mission IV (Remark 3.1).
Preamble
import Definitions.Def_UnderstandingML_FundamentalProof open MeasureTheory
Formal statement
namespace UnderstandingML
/-- **Theorem 28.3** (p. 398). Let `H ⊆ 2^X` with `VCdim(H) = d`. Fix `ε ∈ (0, 1)`, `δ ∈ (0, 1/4)`
and let `m ≥ (8/ε)(2d log(16e/ε) + log(2/δ))`. Then, with probability of at least `1 − δ` over a
choice of `S ∼ D^m` we have that `S` is an `ε`-net for `H`. Measurable `H` with the
countable-approximation property (Remark 3.1). -/
theorem eps_net_theorem {X : Type*} [MeasurableSpace X] (H : Set (X → Bool))
(hH : ∀ h ∈ H, Measurable h) (hsep : PointwiseSeparable H) (d : ℕ) (hd : vcDim H = d)
(D : Measure X) [IsProbabilityMeasure D] (ε δ : ℝ) (hε : 0 < ε) (hε1 : ε < 1) (hδ : 0 < δ)
(hδ1 : δ < 1 / 4) (m : ℕ)
(hm : 8 / ε * (2 * d * Real.log (16 * Real.exp 1 / ε) + Real.log (2 / δ)) ≤ m) :
iidLaw D m {S | ¬ IsEpsNet H D ε S} ≤ ENNReal.ofReal δ := by sorry
end UnderstandingML
Source
Shalev-Shwartz and Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014, doi:10.1017/CBO9781107298019, §28.3 pp. 398-400, Theorem 28.3 with its proof
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.