TY - JOUR
T1 - On the complexity of binary samples
AU - Ratsaby, Joel
PY - 2008/1
Y1 - 2008/1
N2 - Consider a class H of binary functions h: X → {-1,+1} on an interval X = [0, B] ⊂ IR. Define the sample width of h on a finite subset (a sample) S ⊂ X as ωS (h) = min x∈S |ωh (x)| where ωh (x)=h(x) max {a ≥ 0: h(z) = h(x), x - a ≤ z ≤ x + a}. Let double-struck S signℓ be the space of all samples in X of ℓ and consider sets of wide samples, i.e., hypersets which are defined as Aβ, h} = {S ∈ double-struck S signℓ: ωS(h) ≥ β}. Through an application of the Sauer-Shelah result on the density of sets an upper estimate is obtained on the growth function (or trace) of the class {Aβ, h: h ∈ ℋ}, β > 0, i.e., on the number of possible dichotomies obtained by intersecting all hypersets with a fixed collection of samples S ∈ double-struk S signℓ of cardinality m. The estimate is 2∑i=02⌊ B/(2β)⌋ (m-ℓ i).
AB - Consider a class H of binary functions h: X → {-1,+1} on an interval X = [0, B] ⊂ IR. Define the sample width of h on a finite subset (a sample) S ⊂ X as ωS (h) = min x∈S |ωh (x)| where ωh (x)=h(x) max {a ≥ 0: h(z) = h(x), x - a ≤ z ≤ x + a}. Let double-struck S signℓ be the space of all samples in X of ℓ and consider sets of wide samples, i.e., hypersets which are defined as Aβ, h} = {S ∈ double-struck S signℓ: ωS(h) ≥ β}. Through an application of the Sauer-Shelah result on the density of sets an upper estimate is obtained on the growth function (or trace) of the class {Aβ, h: h ∈ ℋ}, β > 0, i.e., on the number of possible dichotomies obtained by intersecting all hypersets with a fixed collection of samples S ∈ double-struk S signℓ of cardinality m. The estimate is 2∑i=02⌊ B/(2β)⌋ (m-ℓ i).
KW - Binary functions
KW - Density of sets
KW - VC-dimension
UR - http://www.scopus.com/inward/record.url?scp=53649090775&partnerID=8YFLogxK
U2 - 10.1007/s10472-008-9096-3
DO - 10.1007/s10472-008-9096-3
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AN - SCOPUS:53649090775
SN - 1012-2443
VL - 52
SP - 55
EP - 65
JO - Annals of Mathematics and Artificial Intelligence
JF - Annals of Mathematics and Artificial Intelligence
IS - 1
ER -