Subject
- Mathematics 286
- Biomedical Engineering 2
- Interdisciplinary Research 2
- Life Sciences 2
- Civil Engineering 1
- Communication 1
- Library Science 1
Creator
- Walter Gautschi, 0000-0001-9184-8899 287
- Mireille Boutin, 0000-0002-0837-6577 3
- Bartlomiej P. Rajwa 2
- Courtney Falk 2
- Jonas Hepp 2
- Murat Dundar 2
- Yellamraju Tarun 2
- Adnan Firoze, 0000-0002-2751-009X 1
- Akshaj Uppala 1
- Austin J. Klasa 1
Type
Date
Total is 303 Results
32-digit values of the first 100 recurrence coefficients for the half-range squared generalized Binet weight function with parameter 1/2
10.4231/R7FT8J7R
10/23/2017
32-digit values of the first 100 recurrence coefficients for the weight function w(x)=log^2(1-a*exp(-x)) on [0,Inf], a = 1/2
Binet weight function Computer Science Mathematics Matlab Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions
32-digit values of the first 100 recurrence coefficients for the Binet weight function
10.4231/R7PC30JZ
10/24/2017
32-digit values of the first 100 recurrence coefficients for the weight function w(x)=-log(1-exp(-|x|)) on [-Inf,Inf]
Binet weight function Computer Science Mathematics Matlab Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions
32-digit values of the first 100 recurrence coefficients for the lower subrange Binet weight function on [0,c], c=1
10.4231/R7P84936
11/02/2017
32-digit values of the first 100 recurrence coefficients for the weight function w(x)=-log(1-exp(-x)) on [0,c], c=1
Binet-like weight functions Computer Science Mathematics Matlab Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions
32-digit values of the first 100 recurrence coefficients for the upper subrange Binet weight function on [c,Inf], c=1
10.4231/R7CZ35CV
10/24/2017
32-digit values of the first 100 recurrence coefficients for the weight function w(x)=-log(1-exp(-x)) on [c,Inf], c=1
Binet-like weight functions Computer Science Mathematics Matlab Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions
32-digit values of the first 100 recurrence coefficients for the lower symmetric subrange Binet weight function on [-c,c], c=1
10.4231/R7862DNF
10/24/2017
32-digit values of the first 100 recurrence coefficients for the weight function w(x)=-log(1-exp(-|x|)) on [-c,c], c=1
Binet weight function Computer Science Mathematics Matlab Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions
32-digit values of the first 100 recurrence coefficients for the lower symmetric subrange Binet weight function on [-c,c], c=1
10.4231/R7KP80BB
01/10/2018
32-digit values of the first 100 recurrence coefficients for the weight function w(x)=-log(1-exp(-|x|)) on [-c,c], c=1
Binet weight function Computer Science Mathematics Matlab Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions
32-digit values of the first 100 recurrence coefficients for the lower subrange Binet weight function on [0,c], c=1
10.4231/R7QF8R2P
01/10/2018
32-digit values of the first 100 recurrence coefficients for the weight function w(x)=-log(1-exp(-x)) on [0,c], c=1
Binet-like weight functions Computer Science Mathematics Matlab Modification algorithms for orthogonal polynomials Orthogonal polynomials Walter Gautschi Archives weight functions
Graph of Flickr Photo-Sharing Social Network Crawled in May 2006
10.4231/D39P2W550
02/22/2012
Crawl of the Flickr photo-sharing social network from May 2006 returning a graph with 820,878 nodes and 9,837,214 edges. Dataset is distributed as a SMAT file with README file with code to read file in Python and MATLAB.
Controlling the cost and increasing the utility of network measurement infrastructures
10.4231/D3TH8BM5D
09/13/2012
Dissertation on the cost and utility of network measurement infrastructures, including mechanisms for admission control, identifying measurement patterns that will benefit from inference, and selecting vantage points for wide-scale measurement.
Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data
10.4231/R7N58J9Z
Baichuan Zhang , Bartlomiej P. Rajwa , Murat Dundar , Qiang Kou , Yicheng He
10/01/2015
A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity
bacterial colonies BARDOT Biomedical Engineering Computer Science deep learning Elastic light scattering Interdisciplinary Research K-Means Clustering Life Sciences Machine Learning representation learning
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