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In machine learning, support vector machines (SVMs, also support vector networks [1]) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis.
Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).
In probability theory, the central limit theorem (CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution.
Hasty generalization often follows a pattern such as: X is true for A. X is true for B. Therefore, X is true for C, D, etc. While never a valid logical deduction, if such an inference can be made on statistical grounds, it may nonetheless be convincing. This is because with enough empirical evidence, the generalization is no longer a hasty one.
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False precision (also called overprecision, fake precision, misplaced precision and spurious precision) occurs when numerical data are presented in a manner that implies better precision than is justified; since precision is a limit to accuracy (in the ISO definition of accuracy), this often leads to overconfidence in the accuracy, named precision bias.
Pruning processes can be divided into two types (pre- and post-pruning). Pre-pruning procedures prevent a complete induction of the training set by replacing a stop criterion in the induction algorithm (e.g. max. Tree depth or information gain (Attr)> minGain).
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]