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pro vyhledávání: '"Angela Y. Wu"'
Autor:
Yasunori Nagahama, Charuta Joshi, Brian Dlouhy, Angela Y. Wu, Taylor J. Abel, Gary Baumbach, Hiroto Kawasaki
Publikováno v:
Epilepsy and Behavior Case Reports, Vol 7, Iss C, Pp 24-27 (2017)
A 7-year-old previously healthy girl presented with a left-sided focal seizure without impaired consciousness and subsequently developed epilepsia partialis continua. Initial MRI was normal, and the subsequent images only showed a focal T2/FLAIR hype
Externí odkaz:
https://doaj.org/article/00c58738da5f41a996c51c9dfcc72501
Publikováno v:
Computational Statistics & Data Analysis. 99:148-170
Autor:
Hiroto Kawasaki, Taylor J. Abel, Yasunori Nagahama, Charuta Joshi, Brian J. Dlouhy, Gary L. Baumbach, Angela Y. Wu
Publikováno v:
Epilepsy & Behavior Case Reports
Epilepsy and Behavior Case Reports, Vol 7, Iss C, Pp 24-27 (2017)
Epilepsy and Behavior Case Reports, Vol 7, Iss C, Pp 24-27 (2017)
A 7-year-old previously healthy girl presented with a left-sided focal seizure without impaired consciousness and subsequently developed epilepsia partialis continua. Initial MRI was normal, and the subsequent images only showed a focal T2/FLAIR hype
Publikováno v:
Algorithmica. 69:148-183
The linear least trimmed squares (LTS) estimator is a statistical technique for fitting a linear model to a set of points. Given a set of n points in ℝ d and given an integer trimming parameter h≤n, LTS involves computing the (d−1)-dimensional
Publikováno v:
Computational Statistics & Data Analysis. 51:2461-2486
The problem of fitting a straight line to a finite collection of points in the plane is an important problem in statistical estimation. Robust estimators are widely used because of their lack of sensitivity to outlying data points. The least median-o
Autor:
Tapas Kanungo, Nathan S. Netanyahu, David M. Mount, Ruth Silverman, Christine D. Piatko, Angela Y. Wu
Publikováno v:
Symposium on Computational Geometry
In k-means clustering we are given a set of n data points in d-dimensional space Rd and an integer k, and the problem is to determine a set of k points in Rd, called centers, to minimize the mean squared distance from each data point to its nearest c
Autor:
Angela Y. Wu, David M. Mount, Tapas Kanungo, Ruth Silverman, Nathan S. Netanyahu, Christine D. Piatko
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 24:881-892
In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to i
Autor:
David M. Mount, Tapas Kanungo, Nathan S. Netanyahu, Angela Y. Wu, Ruth Silverman, Christine D. Piatko
Publikováno v:
IEEE Transactions on Image Processing. 10:1826-1835
Computing discrete two-dimensional (2-D) convolutions is an important problem in image processing. In mathematical morphology, an important variant is that of computing binary convolutions, where the kernel of the convolution is a 0-1 valued function
Publikováno v:
Computational Geometry. 17:97-119
The nearest neighbor problem is that of preprocessing a set P of n data points in Rd so that, given any query point q, the closest point in P to q can be determined efficiently. In the chromatic nearest neighbor problem, each point of P is assigned a
Publikováno v:
International Journal of Computational Geometry & Applications. 10:593-608
Given a set P of n points in Rd, a fundamental problem in computational geometry is concerned with finding the smallest shape of some type that encloses all the points of P. Well-known instances of this problem include finding the smallest enclosing