Приклади вживання Regularization Англійська мовою та їх переклад на Українською
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Regularization, in mathematics and statistics
The regularization parameters λ A{\displaystyle\lambda_{A}}
by augmenting the cost function as in Tikhonov regularization.
In artificial intelligence, this principle is called regularization and it works by creating simple computer models that prioritize core information but eliminate specific details,
for several boosting algorithms(including AdaBoost), that regularization via early stopping can provide guarantees of consistency,
retention of highly qualified foreign specialists, and regularization of irregular migrants who have families
The minimization algorithm can penalize more complex functions(known as Tikhonov regularization), or the hypothesis space can be constrained, either explicitly in the form of the functions or by adding constraints to the minimization function(Ivanov regularization).
retention of highly qualified foreign specialists, and regularization of irregular migrants who have families
In light of the above discussion, we see that the SVM technique is equivalent to empirical risk minimization with Tikhonov regularization, where in this case the loss function is the hinge loss.
The transliteration of names is carried out in accordance with the requirements of the Decree of the Cabinet of Ministers of Ukraine“On the regularization of the transliteration of the Ukrainian alphabet in Latin” No. 55 of January 27, 2010.
an Ahmad-Cohen neighbour scheme and regularization of close encounters.
There are also other ways to perform regularization.
together with L1 regularization on the weights to enable sparsity(i.e.,
Know: regularization methods for linear and non-linear ill-posed problems;
The second is to use some form of regularization.
Early stopping can be viewed as regularization in time.
The use of methods of the nonlinear spatial-temporal data regularization for the analysis of meteorological observations.
Regularization is, therefore, especially important for these methods.
Regularization can solve the overfitting problem
Additional terms in the training cost function can easily perform regularization of the final model.