Is PACLearner For mono ε
Cslib.MachineLearning.PACLearning.IsPACLearnerFor.mono_ε
Plain-language statement
A PAC learner with accuracy ε₁ is also a PAC learner with any weaker accuracy ε₂ ≥ ε₁: the bad event {error > opt + ε} only shrinks.
Exact Lean statement
theorem IsPACLearnerFor.mono_ε {m : ℕ} {δ : Set.Ioo (0 : ℝ≥0) 1}
{ε₁ ε₂ : Set.Ioo (0 : ℝ≥0) 1} (hε : ε₁.val ≤ ε₂.val)
{C : ConceptClass α β} {𝒟 : Set (Measure (α × β))}
(h : IsPACLearnerFor m ε₁ δ C 𝒟) :
IsPACLearnerFor m ε₂ δ C 𝒟Formal artifact
Lean source
theorem IsPACLearnerFor.mono_ε {m : ℕ} {δ : Set.Ioo (0 : ℝ≥0) 1} {ε₁ ε₂ : Set.Ioo (0 : ℝ≥0) 1} (hε : ε₁.val ≤ ε₂.val) {C : ConceptClass α β} {𝒟 : Set (Measure (α × β))} (h : IsPACLearnerFor m ε₁ δ C 𝒟) : IsPACLearnerFor m ε₂ δ C 𝒟 := by obtain ⟨A, hA⟩ := h refine ⟨A, fun D inst hD => le_trans (measure_mono ?_) (@hA D inst hD)⟩ intro S hS have hε' : (↑ε₁.val : ℝ≥0∞) ≤ ↑ε₂.val := by exact_mod_cast hε calc optimalError D C + (↑ε₁.val : ℝ≥0∞) ≤ optimalError D C + ↑ε₂.val := by gcongr _ < error D (A S) := hS- Project
- Lean Computer Science Library
- License
- Apache-2.0
- Commit
- f36649cff2c9
- Source
- Cslib/MachineLearning/PACLearning/Defs.lean:228-239
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Related declarations
Unique minimal
Cslib.Automata.DA.FinAcc.unique_minimal
Plain-language statement
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Source project: Lean Computer Science Library
Person-level attribution pending.
Buchi Family cover
Cslib.Automata.NA.Buchi.buchiFamily_cover
Project documentation
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Source project: Lean Computer Science Library
Person-level attribution pending.
Buchi Family saturation
Cslib.Automata.NA.Buchi.buchiFamily_saturation
Plain-language statement
na.buchiFamily saturates the ω-language accepted by na.
Source project: Lean Computer Science Library
Person-level attribution pending.