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- Stability_(learning_theory) abstract "Stability, also known as algorithmic stability, is a notion in computational learning theory of how a machine learning algorithm is perturbed by small changes to its inputs. A stable learning algorithm is one for which the prediction does not change much when the training data is modified slightly. For instance, consider a machine learning algorithm that is being trained to recognize handwritten letters of the alphabet, using 1000 examples of handwritten letters and their labels ("A" to "Z") as a training set. One way to modify this training set is to leave out an example, so that only 999 examples of handwritten letters and their labels are available. A stable learning algorithm would produce a similar classifier with both the 1000-element and 999-element training sets.Stability can be studied for many types of learning problems, from language learning to inverse problems in physics and engineering, as it is a property of the learning process rather than the type of information being learned. The study of stability gained importance in computational learning theory in the 2000s when it was shown to have a connection with generalization. It was shown that for large classes of learning algorithms, notably empirical risk minimization algorithms, certain types of stability ensure good generalization.".
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- Stability_(learning_theory) wikiPageWikiLink Andrey_Nikolayevich_Tikhonov.
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- Stability_(learning_theory) wikiPageWikiLink Computational_learning_theory.
- Stability_(learning_theory) wikiPageWikiLink Consistent_estimator.
- Stability_(learning_theory) wikiPageWikiLink Empirical_risk_minimization.
- Stability_(learning_theory) wikiPageWikiLink Inverse_problem.
- Stability_(learning_theory) wikiPageWikiLink Leave-one-out_error.
- Stability_(learning_theory) wikiPageWikiLink Leave_one_out_error.
- Stability_(learning_theory) wikiPageWikiLink Linear_regression.
- Stability_(learning_theory) wikiPageWikiLink Machine_learning.
- Stability_(learning_theory) wikiPageWikiLink Natural_language_processing.
- Stability_(learning_theory) wikiPageWikiLink Nature_(journal).
- Stability_(learning_theory) wikiPageWikiLink Nature_journal.
- Stability_(learning_theory) wikiPageWikiLink Regularization_(machine_learning).
- Stability_(learning_theory) wikiPageWikiLink Regularization_(mathematics).
- Stability_(learning_theory) wikiPageWikiLink Sensitivity_analysis.
- Stability_(learning_theory) wikiPageWikiLink Statistical_classification.
- Stability_(learning_theory) wikiPageWikiLink Supervised_learning.
- Stability_(learning_theory) wikiPageWikiLink Support_Vector_Machine.
- Stability_(learning_theory) wikiPageWikiLink Support_vector_machine.
- Stability_(learning_theory) wikiPageWikiLink Uniform_convergence.
- Stability_(learning_theory) wikiPageWikiLink VC_dimension.
- Stability_(learning_theory) wikiPageWikiLink VC_theory.
- Stability_(learning_theory) wikiPageWikiLink Vapnik–Chervonenkis_theory.
- Stability_(learning_theory) wikiPageWikiLink Vladimir_Vapnik.
- Stability_(learning_theory) wikiPageWikiLinkText "Stability (learning theory)".
- Stability_(learning_theory) wikiPageWikiLinkText "stability".
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- Stability_(learning_theory) wikiPageUsesTemplate Template:Refend.
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- Stability_(learning_theory) subject Category:Learning.
- Stability_(learning_theory) subject Category:Machine_learning.
- Stability_(learning_theory) hypernym Notion.
- Stability_(learning_theory) type Country.
- Stability_(learning_theory) comment "Stability, also known as algorithmic stability, is a notion in computational learning theory of how a machine learning algorithm is perturbed by small changes to its inputs. A stable learning algorithm is one for which the prediction does not change much when the training data is modified slightly.".
- Stability_(learning_theory) label "Stability (learning theory)".
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