Datasets

Subject
Creator
Type
Date

subject: Computer Science date: 2016

Total is 71 Results
32-digit values of the first 100 recurrence coefficients for the Fermi-Dirac-type weight function with exponent r=2

10.4231/R77S7KR9

Walter Gautschi ORCID logo

12/09/2016

32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=[1/(exp(x)+1)]^r on [0,Inf], r=2

Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions

32-digit values of the first 100 recurrence coefficients for the Fermi-Dirac-type weight function with exponent r=4

10.4231/R708639Z

Walter Gautschi ORCID logo

12/09/2016

32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=[1/(exp(x)+1)]^r on [0,Inf], r=4

Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions

32-digit values of the first 100 recurrence coefficients for the half-range Freud weight function with exponents mu=0, nu=4

10.4231/R7416V2R

Walter Gautschi ORCID logo

12/09/2016

32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^mu*exp(-x^nu) on [0,Inf], mu=0, nu=4

Computer Science Freud weight function Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions

32-digit values of the first 100 recurrence coefficients for the Schroedinger weight function with exponent mu=5

10.4231/R7QR4V3Z

Walter Gautschi ORCID logo

12/09/2016

32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^mu*exp(-x^4/16) on [0,Inf], mu=5

Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Schroedinger weight function Walter Gautschi Archives weight functions

Generalized Hermite polynomials

10.4231/R79K4878

Walter Gautschi ORCID logo

12/15/2016

Matlab routine for the first N recurrence coefficients of generalized Hermite polynomials

Computer Science Hermite polynomials Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions

Jacobi polynomials

10.4231/R7PR7SZ5

Walter Gautschi ORCID logo

12/15/2016

Matlab routines for the first N recurrence coefficients of Jacobi polynomials

Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions

Generalized Laguerre polynomials

10.4231/R7K07280

Walter Gautschi ORCID logo

12/15/2016

Matlab routines for the first N recurrence coefficients of generalized Laguerre polynomials

Computer Science Laguerre polynomials Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions

Meixner-Pollaczek polynomials

10.4231/R75T3HG3

Walter Gautschi ORCID logo

12/15/2016

Matlab routine for the first N recurrence coefficients of Meixner-Pollaczek polynomials

Computer Science Mathematics Meixner-Pollaczek polynomials Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions

OPCBSPL: Orthogonal polynomials relative to cardinal B-spline weight functions

10.4231/R7NG4NKC

Walter Gautschi ORCID logo

10/28/2016

A stable and efficient discretization procedure is developed to compute recurrence coefficients for orthogonal polynomials whose weight function is a polynomial cardinal B-spline of order greater than, or equal to, one.

B-spline Computer Science Mathematics Modification algorithms for orthogonal polynomials Orthogonal polynomials polynomials Walter Gautschi Archives weight functions

Code and Dataset for Pattern Recognition Benchmarks

10.4231/R7610X8M

Jonas Hepp , Mireille Boutin ORCID logo , Yellamraju Tarun

02/03/2016

This code computed a sequence of bounds for the error rate of a pattern recognition method. The bounds correspond to the error rate that one would expect to achieve by simply selecting features at random and thresholding the feature (TARP) approach.

classification Computer Science Electrical and Computer Engineering feature evaluation image processing image recognition Pattern Recognition Pedestrian Classification

Display #

Results 1 - 10 of 71