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What (who) is optimal regularization - definition
CLASS OF MATHEMATICAL PROBLEMS CONCERNED WITH CHOOSING AN OPTIMAL TIME TO TAKE A PARTICULAR ACTION
Optimal Stopping; Optimal Stopping problem
Manifold regularization
![Manifold regularization can classify data when labeled data (black and white circles) are sparse, by taking advantage of unlabeled data (gray circles). Without many labeled data points, [[supervised learning]] algorithms can only learn very simple decision boundaries (top panel). Manifold learning can draw a decision boundary between the natural classes of the unlabeled data, under the assumption that close-together points probably belong to the same class, and so the decision boundary should avoid areas with many unlabeled points. This is one version of [[semi-supervised learning]].](https://commons.wikimedia.org/wiki/Special:FilePath/Example of unlabeled data in semisupervised learning.png?width=200 "Manifold regularization can classify data when labeled data (black and white circles) are sparse, by taking advantage of unlabeled data (gray circles). Without many labeled data points, [[supervised learning]] algorithms can only learn very simple decision boundaries (top panel). Manifold learning can draw a decision boundary between the natural classes of the unlabeled data, under the assumption that close-together points probably belong to the same class, and so the decision boundary should avoid areas with many unlabeled points. This is one version of [[semi-supervised learning]].")
![A two-dimensional [[manifold]] embedded in three-dimensional space (left). Manifold regularization attempts to learn a function that is smooth on the unrolled manifold (right).](https://commons.wikimedia.org/wiki/Special:FilePath/Swissroll manifold unrolled.png?width=200 "A two-dimensional [[manifold]] embedded in three-dimensional space (left). Manifold regularization attempts to learn a function that is smooth on the unrolled manifold (right).")
User:Mkbehr/Manifold Regularization; Draft:Manifold regularization
In machine learning, Manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that dataset. In many machine learning problems, the data to be learned do not cover the entire input space.
Matrix regularization
User:Cleary83/sandbox; Draft:Matrix regularization
In the field of statistical learning theory, matrix regularization generalizes notions of vector regularization to cases where the object to be learned is a matrix. The purpose of regularization is to enforce conditions, for example sparsity or smoothness, that can produce stable predictive functions.
Optimal decision
DECISION THAT LEADS TO THE BEST OUTCOME IN DECISION THEORY
User:Winterfors/Optimal Decision; Optimal Decision
An optimal decision is a decision that leads to at least as good a known or expected outcome as all other available decision options. It is an important concept in decision theory.
Wikipedia
Optimal stopping
In mathematics, the theory of optimal stopping or early stopping is concerned with the problem of choosing a time to take a particular action, in order to maximise an expected reward or minimise an expected cost. Optimal stopping problems can be found in areas of statistics, economics, and mathematical finance (related to the pricing of American options). A key example of an optimal stopping problem is the secretary problem. Optimal stopping problems can often be written in the form of a Bellman equation, and are therefore often solved using dynamic programming.
