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The basic form of a linear predictor function () for data point i (consisting of p explanatory variables), for i = 1, ..., n, is = + + +,where , for k = 1, ..., p, is the value of the k-th explanatory variable for data point i, and , …, are the coefficients (regression coefficients, weights, etc.) indicating the relative effect of a particular explanatory variable on the outcome.
Screenshot of TDE software programs mostly localized to Chinese (traditional). In computing, internationalization and localization or internationalisation and localisation (), often abbreviated i18n and l10n respectively, [1] are means of adapting computer software to different languages, regional peculiarities and technical requirements of a target locale.
In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis. When representing images, the feature values ...
Video game localization, preparation of video games for other locales; Dub localization and subtitle localization, the adaptation of a movie or television series for another audience; Indigenization, the process of adopting and integrating elements of a local culture, including language, customs, and names, often to better align with the local ...
In machine learning, feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret, [1] shorter training times, [2]
The expected value of the information gain is the mutual information (;) of and – i.e. the reduction in the entropy of achieved by learning the state of the random variable . In machine learning, this concept can be used to define a preferred sequence of attributes to investigate to most rapidly narrow down the state of X.
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Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was input directly (compare transfer learning). [1] In machine learning (ML), feature learning or representation learning [2] is a set of techniques that allow a system to automatically discover the representations needed ...