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In statistics, multivariate adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. [1] It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models nonlinearities and interactions between variables.
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory ...
Huber loss function; Human subject research; Hurst exponent; Hyper-exponential distribution; Hyper-Graeco-Latin square design; Hyperbolic distribution; Hyperbolic secant distribution; Hypergeometric distribution; Hyperparameter (Bayesian statistics) Hyperparameter (machine learning) Hyperprior; Hypoexponential distribution
Among the most used adaptive algorithms is the Widrow-Hoff’s least mean squares (LMS), which represents a class of stochastic gradient-descent algorithms used in adaptive filtering and machine learning. In adaptive filtering the LMS is used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean ...
AdaBoost is adaptive in the sense that subsequent weak learners (models) are adjusted in favor of instances misclassified by previous models. In some problems, it can be less susceptible to overfitting than other learning algorithms. The individual learners can be weak, but as long as the performance of each one is slightly better than random ...
For example, a covariate may be multivariate and the corresponding a smooth function of several variables, or might be the function mapping the level of a factor to the value of a random effect. Another example is a varying coefficient (geographic regression) term such as z j f j ( x j ) {\displaystyle z_{j}f_{j}(x_{j})} where z j ...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
In mathematics, a Relevance Vector Machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic classification. [1] A greedy optimisation procedure and thus fast version were subsequently developed.