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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 practice, as in most of statistics, the difficulties and subtleties are associated with modeling the probability distributions effectively—in this case, ⁡ (= =). The Bayes classifier is a useful benchmark in statistical classification.

  4. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    The model is initially fit on a training data set, [3] which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. [4] The model (e.g. a naive Bayes classifier) is trained on the training data set using a supervised learning method, for example using ...

  5. Bayes error rate - Wikipedia

    en.wikipedia.org/wiki/Bayes_error_rate

    Download QR code; Print/export ... This solution is known as the Bayes classifier. ... Naive Bayes classifier; References

  6. Discriminative model - Wikipedia

    en.wikipedia.org/wiki/Discriminative_model

    However, in Ulusoy and Bishop's joint work, Comparison of Generative and Discriminative Techniques for Object Detection and Classification, they state that the above statement is true only when the model is the appropriate one for data (i.e.the data distribution is correctly modeled by the generative model).

  7. 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

  8. Relevance vector machine - Wikipedia

    en.wikipedia.org/wiki/Relevance_vector_machine

    where is the kernel function (usually Gaussian), are the variances of the prior on the weight vector (,), and , …, are the input vectors of the training set. [ 4 ] Compared to that of support vector machines (SVM), the Bayesian formulation of the RVM avoids the set of free parameters of the SVM (that usually require cross-validation-based ...

  9. Additive smoothing - Wikipedia

    en.wikipedia.org/wiki/Additive_smoothing

    Download QR code; Print/export ... though in practice a smaller value is typically chosen. ... Additive smoothing is commonly a component of naive Bayes classifiers.