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Regularization (mathematics) - Wikipedia

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In mathematics, statistics, finance,[1] and computer science, particularly in machine learning and inverse problems, regularization is a process that converts the answer of a problem to a simpler one. It is often used in solving ill-posed problems or to prevent overfitting.[2] Although regularization procedures can be divided in many ways, the following delineation is particularly helpful: In explicit regularization, independent of the problem or model, there is always a data term, that corresponds to a likelihood of the measurement and a regularization term that corresponds to a prior. By combining both using Bayesian statistics, one can compute a posterior, that includes both information sources and therefore stabilizes the estimation process. By trading off both objectives, one chooses to be more addictive to the data or to enforce regularization (to prevent overfitting). There is a whole research branch dealing with all possible regularizations. In practice, one usually tries a spe

Regularization (mathematics) - Wikipedia Jump to content From Wikipedia, the free encyclopedia Technique to make a model more generalizable and transferable The green and blue functions both incur zero loss on the given data points. A learned model can be induced to prefer the green function, which may generalize better to more points drawn from the underlying unknown distribution, by adjusting λ {\displaystyle \lambda } , the weight of the regularization term. In mathematics , statistics , finance , [ 1 ] and computer science , particularly in machine learning and inverse probl

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