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41 relations: Artificial neural network, Basis function, Boosting (machine learning), Brute-force search, Cross-validation (statistics), Decision tree learning, Dependent and independent variables, Errors and residuals, Generalized additive model, Generalized linear model, Grace Wahba, Greedy algorithm, Heuristic, Ice hockey stick, Inverse problem, Jerome H. Friedman, Linear model, Linear regression, Local regression, Logistic regression, Multicollinearity, Nonlinear regression, Nonparametric regression, Overfitting, Piecewise, Polynomial and rational function modeling, R (programming language), Ramp function, Random forest, Rectifier (neural networks), Recursive partitioning, Regression analysis, Regularization (mathematics), Residual sum of squares, Segmented regression, Smoothing spline, Spline (mathematics), Spline interpolation, Statistics, Support vector machine, Well-posed problem.
Artificial neural network
Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
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Basis function
In mathematics, a basis function is an element of a particular basis for a function space.
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Boosting (machine learning)
Boosting is a machine learning ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones.
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Brute-force search
In computer science, brute-force search or exhaustive search, also known as generate and test, is a very general problem-solving technique that consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the problem's statement.
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Cross-validation (statistics)
Cross-validation, sometimes called rotation estimation, or out-of-sample testing is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set.
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Decision tree learning
Decision tree learning uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves).
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Dependent and independent variables
In mathematical modeling, statistical modeling and experimental sciences, the values of dependent variables depend on the values of independent variables.
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Errors and residuals
In statistics and optimization, errors and residuals are two closely related and easily confused measures of the deviation of an observed value of an element of a statistical sample from its "theoretical value".
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Generalized additive model
In statistics, a generalized additive model (GAM) is a generalized linear model in which the linear predictor depends linearly on unknown smooth functions of some predictor variables, and interest focuses on inference about these smooth functions.
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Generalized linear model
In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution.
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Grace Wahba
Grace Wahba (born August 3, 1934) is the I. J. Schoenberg-Hilldale Professor of Statistics at the University of Wisconsin–Madison.
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Greedy algorithm
A greedy algorithm is an algorithmic paradigm that follows the problem solving heuristic of making the locally optimal choice at each stage with the intent of finding a global optimum.
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Heuristic
A heuristic technique (εὑρίσκω, "find" or "discover"), often called simply a heuristic, is any approach to problem solving, learning, or discovery that employs a practical method, not guaranteed to be optimal, perfect, logical, or rational, but instead sufficient for reaching an immediate goal.
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Ice hockey stick
An ice hockey stick is a piece of equipment used in ice hockey to shoot, pass, and carry the puck across the ice.
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Inverse problem
An inverse problem in science is the process of calculating from a set of observations the causal factors that produced them: for example, calculating an image in X-ray computed tomography, source reconstruction in acoustics, or calculating the density of the Earth from measurements of its gravity field.
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Jerome H. Friedman
Jerome Harold Friedman (born 1939) is an American statistician, consultant and Professor of Statistics at Stanford University, known for his contributions in the field of statistics and data mining.
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Linear model
In statistics, the term linear model is used in different ways according to the context.
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Linear regression
In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).
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Local regression
LOESS and LOWESS (locally weighted scatterplot smoothing) are two strongly related non-parametric regression methods that combine multiple regression models in a ''k''-nearest-neighbor-based meta-model.
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Logistic regression
In statistics, the logistic model (or logit model) is a statistical model that is usually taken to apply to a binary dependent variable.
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Multicollinearity
In statistics, multicollinearity (also collinearity) is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy.
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Nonlinear regression
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables.
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Nonparametric regression
Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to information derived from the data.
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Overfitting
In statistics, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably".
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Piecewise
In mathematics, a piecewise-defined function (also called a piecewise function or a hybrid function) is a function defined by multiple sub-functions, each sub-function applying to a certain interval of the main function's domain, a sub-domain.
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Polynomial and rational function modeling
In statistical modeling (especially process modeling), polynomial functions and rational functions are sometimes used as an empirical technique for curve fitting.
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R (programming language)
R is a programming language and free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing.
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Ramp function
The ramp function is a unary real function, whose graph is shaped like a ramp.
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Random forest
Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
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Rectifier (neural networks)
In the context of artificial neural networks, the rectifier is an activation function defined as the positive part of its argument: f(x).
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Recursive partitioning
Recursive partitioning is a statistical method for multivariable analysis.
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Regression analysis
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables.
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Regularization (mathematics)
In mathematics, statistics, and computer science, particularly in the fields of machine learning and inverse problems, regularization is a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting.
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Residual sum of squares
In statistics, the residual sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared errors of prediction (SSE), is the sum of the squares of residuals (deviations predicted from actual empirical values of data).
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Segmented regression
Segmented regression, also known as piecewise regression or "broken-stick regression", is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval.
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Smoothing spline
Smoothing splines are function estimates, \hat f(x), obtained from a set of noisy observations y_i of the target f(x_i), in order to balance a measure of goodness of fit of \hat f(x_i) to y_i with a derivative based measure of the smoothness of \hat f(x).
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Spline (mathematics)
In mathematics, a spline is a function defined piecewise by polynomials.
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Spline interpolation
In the mathematical field of numerical analysis, Spline interpolation is a form of interpolation where the interpolant is a special type of piecewise polynomial called a spline.
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Statistics
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.
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Support vector machine
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
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Well-posed problem
The mathematical term well-posed problem stems from a definition given by Jacques Hadamard.
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References
[1] https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines
