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Rather than have each use of the template raise an error, this template uses the {{{authors}}} instead. Unfortunately, the use of this parameter is discouraged because it does not contribute to a citation's metadata, and users consuming cs1|2 citations via the metadata will find that the citation is missing authors.
Like approximate entropy (ApEn), Sample entropy (SampEn) is a measure of complexity. [1] But it does not include self-similar patterns as ApEn does. For a given embedding dimension, tolerance and number of data points, SampEn is the negative natural logarithm of the probability that if two sets of simultaneous data points of length have distance < then two sets of simultaneous data points of ...
Relief is an algorithm developed by Kira and Rendell in 1992 that takes a filter-method approach to feature selection that is notably sensitive to feature interactions. [1] [2] It was originally designed for application to binary classification problems with discrete or numerical features.
The 2024 version of this article was updated by an external expert under a dual publication model. The corresponding academic peer reviewed article was published in and can be cited as: "Academic-written review/testcases". {{ cite journal }}: Cite journal requires |journal= (help) ‹ The template below (Test case) is being considered for merging with Testcase. See templates for discussion to ...
Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, when outliers are to be accorded no influence [clarify] on the values of the estimates. Therefore, it also can be interpreted as an outlier detection method. [1]
Some programs (such as MATLAB toolboxes) that design filters with real-valued coefficients prefer the Nyquist frequency (/) as the frequency reference, which changes the numeric range that represents frequencies of interest from [,] cycle/sample to [,] half-cycle/sample. Therefore, the normalized frequency unit is important when converting ...
This article reads like a scientific review article and potentially contains biased syntheses of primary sources. Please replace inadequate primary references with secondary sources. See the talk page for details.
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.