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  2. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    Abstractly, naive Bayes is a conditional probability model: it assigns probabilities (, …,) for each of the K possible outcomes or classes given a problem instance to be classified, represented by a vector = (, …,) encoding some n features (independent variables).

  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. Discriminative model - Wikipedia

    en.wikipedia.org/wiki/Discriminative_model

    Generative model approaches which uses a joint probability distribution instead, include naive Bayes classifiers, Gaussian mixture models, variational autoencoders, generative adversarial networks and others.

  5. Bayes error rate - Wikipedia

    en.wikipedia.org/wiki/Bayes_error_rate

    Naive Bayes classifier; References This page was last edited on 17 December 2024, at 03:38 (UTC). Text is available under the ...

  6. Probabilistic classification - Wikipedia

    en.wikipedia.org/wiki/Probabilistic_classification

    Binary probabilistic classifiers are also called binary regression models in statistics. In econometrics, probabilistic classification in general is called discrete choice. Some classification models, such as naive Bayes, logistic regression and multilayer perceptrons (when trained under an appropriate loss function) are

  7. 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 ...

  8. Linear classifier - Wikipedia

    en.wikipedia.org/wiki/Linear_classifier

    Naive Bayes classifier with multinomial or multivariate Bernoulli event models. The second set of methods includes discriminative models, which attempt to maximize the quality of the output on a training set. Additional terms in the training cost function can easily perform regularization of the final model. Examples of discriminative training ...

  9. Bayesian programming - Wikipedia

    en.wikipedia.org/wiki/Bayesian_programming

    The transition model () and the observation model () are both specified using Gaussian laws with means that are linear functions of the conditioning variables. With these hypotheses and by using the recursive formula, it is possible to solve the inference problem analytically to answer the usual P ( S T ∣ O 0 ∧ ⋯ ∧ O T ∧ π ...