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Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested.
Context-free languages are a category of languages (sometimes termed Chomsky Type 2) which can be matched by a sequence of replacement rules, each of which essentially maps each non-terminal element to a sequence of terminal elements and/or other nonterminal elements.
Probabilistic graphical models, a machine learning technique for determining the relationship between different variables, are one of the most commonly used methods for modeling genetic networks. [2] In addition, machine learning has been applied to systems biology problems such as identifying transcription factor binding sites using Markov ...
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 [clarification needed] 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 ...
[1] In the case of variance component estimation, the original data set is replaced by a set of contrasts calculated from the data, and the likelihood function is calculated from the probability distribution of these contrasts, according to the model for the complete data set. In particular, REML is used as a method for fitting linear mixed models.
Suppose that there is an underlying signal {x(t)}, of which an observed signal {r(t)} is available.The observed signal r is related to x via a transformation that may be nonlinear and may involve attenuation, and would usually involve the incorporation of random noise.
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.
It was released alongside PyPy 2.3.1 and bears the same version number. On 21 March 2017, the PyPy project released version 5.7 of both PyPy and PyPy3, with the latter introducing beta-quality support for Python 3.5. [24] On 26 April 2018, version 6.0 was released, with support for Python 2.7 and 3.5 (still beta-quality on Windows). [25]