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  2. Linear discriminant analysis - Wikipedia

    en.wikipedia.org/wiki/Linear_discriminant_analysis

    Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events.

  3. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class. The strength (naivety) of this assumption is what gives the classifier its name. These classifiers are among the simplest Bayesian network models.

  4. Bayes error rate - Wikipedia

    en.wikipedia.org/wiki/Bayes_error_rate

    where x is the instance, [] the expectation value, C k is a class into which an instance is classified, P(C k |x) is the conditional probability of label k for instance x, and L() is the 0–1 loss function:

  5. Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Bayes_classifier

    In theoretical terms, a classifier is a measurable function , with the interpretation that C classifies the point x to the class C (x). The probability of misclassification, or risk, of a classifier C is defined as. The Bayes classifier is. Bayes argmax K ⁡ ∣ {\displaystyle C^ {\text {Bayes}} (x)= {\underset {r\in \ {1,2,\dots ,K ...

  6. k-nearest neighbors algorithm - Wikipedia

    en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

    In k-NN classification, the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.

  7. Decision boundary - Wikipedia

    en.wikipedia.org/wiki/Decision_boundary

    A decision boundary is the region of a problem space in which the output label of a classifier is ambiguous. [1] If the decision surface is a hyperplane, then the classification problem is linear, and the classes are linearly separable. Decision boundaries are not always clear cut. That is, the transition from one class in the feature space to ...

  8. Smoothness - Wikipedia

    en.wikipedia.org/wiki/Smoothness

    A function of class or -function (pronounced C-infinity function) is an infinitely differentiable function, that is, a function that has derivatives of all orders (this implies that all these derivatives are continuous). Generally, the term smooth function refers to a -function. However, it may also mean "sufficiently differentiable" for the ...

  9. Boundary value problem - Wikipedia

    en.wikipedia.org/wiki/Boundary_value_problem

    Boundary value problems are similar to initial value problems.A boundary value problem has conditions specified at the extremes ("boundaries") of the independent variable in the equation whereas an initial value problem has all of the conditions specified at the same value of the independent variable (and that value is at the lower boundary of the domain, thus the term "initial" value).