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  2. Data augmentation - Wikipedia

    en.wikipedia.org/wiki/Data_augmentation

    Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.

  3. Temporal paradox - Wikipedia

    en.wikipedia.org/wiki/Temporal_paradox

    A bootstrap paradox, also known as an information loop, an information paradox, [6] an ontological paradox, [7] or a "predestination paradox" is a paradox of time travel that occurs when any event, such as an action, information, an object, or a person, ultimately causes itself, as a consequence of either retrocausality or time travel.

  4. Talk:Bootstrapping (statistics) - Wikipedia

    en.wikipedia.org/wiki/Talk:Bootstrapping...

    Bootstrap methods are great for inference, but bootstrap aggregation is a method for ensemble learning - i.e. to aggregate collections of models, for robust development using subsamples of the data. To include bagging into bootstrapping is to misunderstand the use of bagging.

  5. Bootstrapping (linguistics) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(linguistics)

    Semantic bootstrapping is a linguistic theory of language acquisition which proposes that children can acquire the syntax of a language by first learning and recognizing semantic elements and building upon, or bootstrapping from, that knowledge. [8] According to Pinker, [8] semantic bootstrapping requires two critical assumptions to hold true:

  6. Confidence interval - Wikipedia

    en.wikipedia.org/wiki/Confidence_interval

    Each row of points is a sample from the same normal distribution. The colored lines are 50% confidence intervals for the mean, μ.At the center of each interval is the sample mean, marked with a diamond.

  7. Out-of-bag error - Wikipedia

    en.wikipedia.org/wiki/Out-of-bag_error

    One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement. The out-of-bag set is all data not chosen in the sampling process. When this process is repeated, such as when building a random forest, many bootstrap samples and OOB sets are created. The OOB sets can be aggregated into one dataset, but each ...

  8. Bootstrapping (electronics) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(electronics)

    In the sense used in this paragraph, bootstrapping an operational amplifier means "using a signal to drive the reference point of the op-amp's power supplies". [5] A more sophisticated use of this rail bootstrapping technique is to alter the non-linear C/V characteristic of the inputs of a JFET op-amp in order to decrease its distortion. [6] [7]

  9. Maximum parsimony (phylogenetics) - Wikipedia

    en.wikipedia.org/wiki/Maximum_parsimony_(phylo...

    In phylogenetics, parsimony is mostly interpreted as favoring the trees that minimize the amount of evolutionary change required (see for example [2]).Alternatively, phylogenetic parsimony can be characterized as favoring the trees that maximize explanatory power by minimizing the number of observed similarities that cannot be explained by inheritance and common descent.