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A model of double descent at the thermodynamic limit has been analyzed using the replica trick, and the result has been confirmed numerically. [ 12 ] Empirical examples
In a learning problem, the goal is to develop a function () that predicts output values for each input datum . The subscript n {\displaystyle n} indicates that the function f n {\displaystyle f_{n}} is developed based on a data set of n {\displaystyle n} data points.
IQ imbalance is a performance-limiting issue in the design of a class of radio receivers known as direct conversion receivers. [a] These translate the received radio frequency (RF, or pass-band) signal directly from the carrier frequency to baseband using a single mixing stage.
In most of these applications, the unknown signal of interest is modeled as a sparse combination of a few atoms from a given dictionary, and this is used as the regularization of the problem. These problems are typically accompanied by a dictionary learning mechanism that aims to fit to best match the model to the given data. The use of ...
Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.
Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. [1] In the context of machine learning and more generally statistical analysis, this may be the selection of a statistical model from a set of candidate models, given data. In the simplest cases, a pre ...
Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.
Empirical risk minimization for a classification problem with a 0-1 loss function is known to be an NP-hard problem even for a relatively simple class of functions such as linear classifiers. [5] Nevertheless, it can be solved efficiently when the minimal empirical risk is zero, i.e., data is linearly separable .