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Manindra Agrawal (born 20 May 1966) is an Indian computer scientist and director of Indian Institute of Technology, Kanpur.He is also a professor at the Department of Computer Science and Engineering at the Indian Institute of Technology, Kanpur. [1]
Let A be a square n × n matrix with n linearly independent eigenvectors q i (where i = 1, ..., n).Then A can be factored as = where Q is the square n × n matrix whose i th column is the eigenvector q i of A, and Λ is the diagonal matrix whose diagonal elements are the corresponding eigenvalues, Λ ii = λ i.
Vedic Mathematics is a book written by Indian Shankaracharya Bharati Krishna Tirtha and first published in 1965. It contains a list of mathematical techniques which were falsely claimed to contain advanced mathematical knowledge. [ 1 ]
A simple example is fitting a line in two dimensions to a set of observations. Assuming that this set contains both inliers, i.e., points which approximately can be fitted to a line, and outliers, points which cannot be fitted to this line, a simple least squares method for line fitting will generally produce a line with a bad fit to the data including inliers and outliers.
Ravi P. Agarwal (born July 10, 1947) is an Indian mathematician, Ph.D. sciences, professor, professor & chairman, Department of Mathematics Texas A&M University-Kingsville, Kingsville, U.S. Agarwal is the author of over 1000 scientific papers as well as 30 monographs. [1]
Goel and Gupta, [10] however, give a rigorous upper bound that makes no approximations and requires no assumptions. They show that the false positive probability for a finite Bloom filter with m bits ( m > 1 {\displaystyle m>1} ), n elements, and k hash functions is at most
GOOWE-ML [23]-based methods: Interpreting the relevance scores of each component of the ensemble as vectors in the label space and solving a least squares problem at the end of each batch, Geometrically-Optimum Online-Weighted Ensemble for Multi-label Classification (GOOWE-ML) is proposed. The ensemble tries to minimize the distance between the ...
Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.