A note on a scale-sensitive dimension of linear bounded functionals in Banach spaces

Theoretical Computer Science

Volumn 261, Issue 1, 2001, Pages 81-90

  Gurvits, Leonid ^a,b  

^a NEC LABORATORIES AMERICA   (United States)

^b Center for Discrete Mathematics and Theoretical Computer Science   (United States)

Author keywords

Banach spaces; Scale sensitive dimension; VC dimension

Indexed keywords

APPROXIMATION THEORY; LEARNING SYSTEMS; SET THEORY;

BANACH SPACES;

COMPUTATION THEORY;

EID: 0034915184     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3975(00)00134-1     Document Type: Conference Paper

References (11)

1. Scale-sensitive dimensions, uniform convergence, and learnability
2. Approximation and estimation bounds for artificial neural networks
3. What size net gives valid generalization?
4. Rate of approximation results motivated by robust neural network learning
5. Ratex of convex approximations in Non-Hibert spaces
6. Approximation and learning of convex superpositions
7. A simple lemma on greedy approximations in Hilbert space and convergence rates for projection pursuit regression and neural network training
8. Efficient distribution-free learning of probabilistic concepts