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  2. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    Example of a naive Bayes classifier depicted as a Bayesian Network. 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.

  3. Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Bayes_classifier

    In statistical classification, the Bayes classifier is the classifier having the smallest probability of misclassification of all classifiers using the same set of features. [ 1 ] Definition

  4. Naive Bayes spam filtering - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_spam_filtering

    Naive Bayes spam filtering is a baseline technique for dealing with spam that can tailor itself to the email needs of individual users and give low false positive spam detection rates that are generally acceptable to users. It is one of the oldest ways of doing spam filtering, with roots in the 1990s.

  5. Generative model - Wikipedia

    en.wikipedia.org/wiki/Generative_model

    Standard examples of each, all of which are linear classifiers, are: generative classifiers: naive Bayes classifier and; linear discriminant analysis; discriminative model: logistic regression; In application to classification, one wishes to go from an observation x to a label y (or probability distribution on labels

  6. Discriminative model - Wikipedia

    en.wikipedia.org/wiki/Discriminative_model

    For example, in object recognition, is likely to be a vector of raw pixels (or features extracted from the raw pixels of the image). Within a probabilistic framework, this is done by modeling the conditional probability distribution P ( y | x ) {\displaystyle P(y|x)} , which can be used for predicting y {\displaystyle y} from x {\displaystyle x} .

  7. Linear classifier - Wikipedia

    en.wikipedia.org/wiki/Linear_classifier

    In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features.Such classifiers work well for practical problems such as document classification, and more generally for problems with many variables (), reaching accuracy levels comparable to non-linear classifiers while taking less time to train and use.

  8. Bayesian classifier - Wikipedia

    en.wikipedia.org/wiki/Bayesian_classifier

    In computer science and statistics, Bayesian classifier may refer to: any classifier based on Bayesian probability; a Bayes classifier, one that always chooses the class of highest posterior probability in case this posterior distribution is modelled by assuming the observables are independent, it is a naive Bayes classifier

  9. Support vector machine - Wikipedia

    en.wikipedia.org/wiki/Support_vector_machine

    Note that is the i-th target (i.e., in this case, 1 or −1), and is the i-th output. This function is zero if the constraint in is satisfied, in other words, if lies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.

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