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Что (кто) такое least access - определение

APPROXIMATION METHOD IN STATISTICS

Method of least squares; Least-squares method; Least-squares estimation; Least-Squares Fitting; Least squares fitting; Sum of Squared Error; Least-squares; Least squares approximation; Least-squares approximation; Least squares method; Least-squares analysis; Least squares fit; Least squares problem; Least-squares problem; LSQF; Principle of least squares; Least-squares fit; Method of Least Squares; Least Squares

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Least squares

The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each individual equation.

https://en.wikipedia.org/wiki/Least_squares

least squares

¦ noun a method of estimating a quantity or fitting a graph to data so as to minimize the sum of the squares of the differences between the observed values and the estimated values.

Principle of least privilege

PRINCIPLE IN COMPUTER SECURITY THAT EVERY MODULE (SUCH AS A PROCESS, A USER, OR A PROGRAM, DEPENDING ON THE SUBJECT) MUST BE ABLE TO ACCESS ONLY THE INFORMATION AND RESOURCES THAT ARE NECESSARY FOR ITS LEGITIMATE PURPOSE

Least privilege; Principle of least authority; Rule of least privilege; Least user access; Least User Access; POLP; Principle of minimum privilege; Principle of Least Authority; Least-privilege User Account; Principle of minimal privilege; LUA bug; Principle of least access

In information security, computer science, and other fields, the principle of least privilege (PoLP), also known as the principle of minimal privilege (PoMP) or the principle of least authority (PoLA), requires that in a particular abstraction layer of a computing environment, every module (such as a process, a user, or a program, depending on the subject) must be able to access only the information and resources that are necessary for its legitimate purpose.

https://en.wikipedia.org/wiki/Principle_of_least_privilege

Least mean squares filter

ALGORITHM

Least mean squares; NLMS; Normalised Least mean squares filter; Normalized Least mean squares filter; Normalized least mean squares filter; Normalised least mean squares filter; LMS filter

Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean square of the error signal (difference between the desired and the actual signal). It is a stochastic gradient descent method in that the filter is only adapted based on the error at the current time.

https://en.wikipedia.org/wiki/Least_mean_squares_filter

Ordinary least squares

METHOD FOR ESTIMATING THE UNKNOWN PARAMETERS IN A LINEAR REGRESSION MODEL

Ordinary least squares regression; Normal equations; Ordinary Least Squares; Ordinary list squares; OLS Regression; Ordinary least square; Standard error of the equation; OLS regression; Ordinary Least Squares Regression; Partitioned regression; Least-squares normal matrix; Large Sample Properties

In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values of the variable being observed) in the input dataset and the output of the (linear) function of the independent variable.

https://en.wikipedia.org/wiki/Ordinary_least_squares

least fixed point

$Partial order on <math>\mathbb{Z}_\bot</math>$

Greatest fixed point; Least fixpoint; Greatest fixpoint

<mathematics> A function f may have many fixed points (x such that f x = x). For example, any value is a fixed point of the identity function, ( x . x). If f is recursive, we can represent it as f = fix F where F is some higher-order function and fix F = F (fix F). The standard denotational semantics of f is then given by the least fixed point of F. This is the least upper bound of the infinite sequence (the ascending Kleene chain) obtained by repeatedly applying F to the totally undefined value, bottom. I.e. fix F = LUB bottom, F bottom, F (F bottom), .... The least fixed point is guaranteed to exist for a continuous function over a cpo. (2005-04-12)

Least developed countries

LIST OF COUNTRIES THAT EXHIBITS THE LOWEST INDICATORS OF SOCIOECONOMIC DEVELOPMENT

Underdeveloped nations; Ldcs; Undeveloped countries; Less economically developed country; Underdeveloped countries; Least developed nations; Less economically developed countries; Poor countries; 4th World Country; Fourth world country; Undeveloped country; Poorer nations; Least Developed Country; Poorest country; Underdeveloped world; Least-developed countries; Least developing country; Least developing countries; Low-income country; Least developed country; Poor country; Impoverished countries; Least Developed Countries

The least developed countries (LDCs) are a list of developing countries that, according to the United Nations, exhibit the lowest indicators of socioeconomic development, with the lowest Human Development Index ratings of all countries in the world. The concept of LDCs originated in the late 1960s and the first group of LDCs was listed by the UN in its resolution 2768 (XXVI) on 18 November 1971.

https://en.wikipedia.org/wiki/Least_developed_countries

Least absolute deviations

STATISTICAL OPTIMALITY CRITERION

Least absolute deviation; Minimum absolute deviation; Minimum absolute deviations; Least-absolute-deviations regression; Sum of absolute deviations; Least absolute errors; Least absolute value; Least absolute residuals; Least absolute values; LAD regression

Least absolute deviations (LAD), also known as least absolute errors (LAE), least absolute residuals (LAR), or least absolute values (LAV), is a statistical optimality criterion and a statistical optimization technique based minimizing the sum of absolute deviations (sum of absolute residuals or sum of absolute errors) or the L1 norm of such values. It is analogous to the least squares technique, except that it is based on absolute values instead of squared values.

https://en.wikipedia.org/wiki/Least_absolute_deviations

Least fixed point

$Partial order on <math>\mathbb{Z}_\bot</math>$

Greatest fixed point; Least fixpoint; Greatest fixpoint

In order theory, a branch of mathematics, the least fixed point (lfp or LFP, sometimes also smallest fixed point) of a function from a partially ordered set to itself is the fixed point which is less than each other fixed point, according to the order of the poset. A function need not have a least fixed point, but if it does then the least fixed point is unique.

https://en.wikipedia.org/wiki/Least_fixed_point

Least trimmed squares

Least Trimmed Squares

Least trimmed squares (LTS), or least trimmed sum of squares, is a robust statistical method that fits a function to a set of data whilst not being unduly affected by the presence of outliers. It is one of a number of methods for robust regression.

https://en.wikipedia.org/wiki/Least_trimmed_squares

Википедия

Least squares

The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each individual equation.

The most important application is in data fitting. When the problem has substantial uncertainties in the independent variable (the x variable), then simple regression and least-squares methods have problems; in such cases, the methodology required for fitting errors-in-variables models may be considered instead of that for least squares.

Least squares problems fall into two categories: linear or ordinary least squares and nonlinear least squares, depending on whether or not the residuals are linear in all unknowns. The linear least-squares problem occurs in statistical regression analysis; it has a closed-form solution. The nonlinear problem is usually solved by iterative refinement; at each iteration the system is approximated by a linear one, and thus the core calculation is similar in both cases.

Polynomial least squares describes the variance in a prediction of the dependent variable as a function of the independent variable and the deviations from the fitted curve.

When the observations come from an exponential family with identity as its natural sufficient statistics and mild-conditions are satisfied (e.g. for normal, exponential, Poisson and binomial distributions), standardized least-squares estimates and maximum-likelihood estimates are identical. The method of least squares can also be derived as a method of moments estimator.

The following discussion is mostly presented in terms of linear functions but the use of least squares is valid and practical for more general families of functions. Also, by iteratively applying local quadratic approximation to the likelihood (through the Fisher information), the least-squares method may be used to fit a generalized linear model.

The least-squares method was officially discovered and published by Adrien-Marie Legendre (1805), though it is usually also co-credited to Carl Friedrich Gauss (1795) who contributed significant theoretical advances to the method and may have previously used it in his work.

https://en.wikipedia.org/wiki/Least squares