Publication Details
Title: VC Dimension and Learnability of Sparse Polynomials and Rational Functions
Author: M. Karpinski and T. Werther
Group: ICSI Technical Reports
Date: November 1989
PDF: http://www.icsi.berkeley.edu/pubs/techreports/tr-89-60.pdf
Overview:
We prove upper and lower bounds on the VC dimension of sparse univariate polynomials over reals, and apply these results to prove uniform learnability of sparse polynomials and rational functions. As another application we solve an open problem of Vapnik [Vapnik 82] on uniform approximation of the general regression functions, a central problem of computational statistics (cf. [Vapnik 82], p. 256).
Bibliographic Information:
ICSI Technical Report TR-89-060
Bibliographic Reference:
M. Karpinski and T. Werther. VC Dimension and Learnability of Sparse Polynomials and Rational Functions. ICSI Technical Report TR-89-060, November 1989
Author: M. Karpinski and T. Werther
Group: ICSI Technical Reports
Date: November 1989
PDF: http://www.icsi.berkeley.edu/pubs/techreports/tr-89-60.pdf
Overview:
We prove upper and lower bounds on the VC dimension of sparse univariate polynomials over reals, and apply these results to prove uniform learnability of sparse polynomials and rational functions. As another application we solve an open problem of Vapnik [Vapnik 82] on uniform approximation of the general regression functions, a central problem of computational statistics (cf. [Vapnik 82], p. 256).
Bibliographic Information:
ICSI Technical Report TR-89-060
Bibliographic Reference:
M. Karpinski and T. Werther. VC Dimension and Learnability of Sparse Polynomials and Rational Functions. ICSI Technical Report TR-89-060, November 1989