AbstractSauer's lemma is extended to classes HN of binary-valued functions h on [n]={1,…,n} which have a margin less than or equal to N on all x∈[n] with h(x)=1, where the margin μh(x) of h at x∈[n] is defined as the largest non-negative integer a such that h is constant on the interval Ia(x)=[x-a,x+a]⊆[n]. Estimates are obtained for the cardinality of classes of binary-valued functions with a margin of at least N on a positive sample S⊆[n]
AbstractThis paper concerns learning binary-valued functions defined on R, and investigates how a pa...
A proof that a concept class is learnable provided the Vapnik—Chervonenkis dimension is finite is gi...
In this thesis we study the generalized Glivenko-Cantelli theorem and its application in mathematica...
AbstractSauer's lemma is extended to classes HN of binary-valued functions h on [n]={1,…,n} which ha...
Sauer’s Lemma is extended to classes HN of binary-valued functions h on [n] = {1,..., n} which have ...
AbstractLet [n]={1,…,n}. For a function h:[n]→{0,1}, x∈[n] and y∈{0,1} define by the width ωh(x,y) o...
For any class of binary functions on [n] = {1,..., n} a classical result by Sauer states a sufficie...
AbstractWe prove a lower bound of Ω((1/ɛ)ln(1/δ)+VCdim(C)/ɛ) on the number of random examples requir...
The Vapnik-Chervonenkis (VC) dimension (also known as the trace number) and the Sauer-Shelah lemma ...
This paper concerns learning binary-valued functions defined on, and investigates how a particular t...
The Vapnik-Chervonenkis (VC) dimension is a combinatorial measure of a certain class of machine lear...
AbstractIn the PAC-learning model, the Vapnik-Chervonenkis (VC) dimension plays the key role to esti...
Statistical learning theory chiefly studies restricted hypothesis classes, particularly those with f...
AbstractWe generalize Sauer's lemma to multivalued functions, proving tight bounds on the cardinalit...
Existing proofs of Vapnik's result on the VC dimension of bounded margin classifiers rely on th...
AbstractThis paper concerns learning binary-valued functions defined on R, and investigates how a pa...
A proof that a concept class is learnable provided the Vapnik—Chervonenkis dimension is finite is gi...
In this thesis we study the generalized Glivenko-Cantelli theorem and its application in mathematica...
AbstractSauer's lemma is extended to classes HN of binary-valued functions h on [n]={1,…,n} which ha...
Sauer’s Lemma is extended to classes HN of binary-valued functions h on [n] = {1,..., n} which have ...
AbstractLet [n]={1,…,n}. For a function h:[n]→{0,1}, x∈[n] and y∈{0,1} define by the width ωh(x,y) o...
For any class of binary functions on [n] = {1,..., n} a classical result by Sauer states a sufficie...
AbstractWe prove a lower bound of Ω((1/ɛ)ln(1/δ)+VCdim(C)/ɛ) on the number of random examples requir...
The Vapnik-Chervonenkis (VC) dimension (also known as the trace number) and the Sauer-Shelah lemma ...
This paper concerns learning binary-valued functions defined on, and investigates how a particular t...
The Vapnik-Chervonenkis (VC) dimension is a combinatorial measure of a certain class of machine lear...
AbstractIn the PAC-learning model, the Vapnik-Chervonenkis (VC) dimension plays the key role to esti...
Statistical learning theory chiefly studies restricted hypothesis classes, particularly those with f...
AbstractWe generalize Sauer's lemma to multivalued functions, proving tight bounds on the cardinalit...
Existing proofs of Vapnik's result on the VC dimension of bounded margin classifiers rely on th...
AbstractThis paper concerns learning binary-valued functions defined on R, and investigates how a pa...
A proof that a concept class is learnable provided the Vapnik—Chervonenkis dimension is finite is gi...
In this thesis we study the generalized Glivenko-Cantelli theorem and its application in mathematica...