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  2. 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

  3. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    The strength (naivety) of this assumption is what gives the classifier its name. These classifiers are among the simplest Bayesian network models. [1] Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem.

  4. Bayesian learning mechanisms - Wikipedia

    en.wikipedia.org/wiki/Bayesian_learning_mechanisms

    Bayesian learning mechanisms are probabilistic causal models [1] used in computer science to research the fundamental underpinnings of machine learning, and in cognitive neuroscience, to model conceptual development. [2] [3]

  5. Bayes error rate - Wikipedia

    en.wikipedia.org/wiki/Bayes_error_rate

    In terms of machine learning and pattern classification, the labels of a set of random observations can be divided into 2 or more classes. Each observation is called an instance and the class it belongs to is the label .

  6. Bayesian inference - Wikipedia

    en.wikipedia.org/wiki/Bayesian_inference

    Bayesian inference (/ ˈ b eɪ z i ə n / BAY-zee-ən or / ˈ b eɪ ʒ ən / BAY-zhən) [1] is a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, given prior evidence, and update it as more information becomes available.

  7. Bayesian hierarchical modeling - Wikipedia

    en.wikipedia.org/wiki/Bayesian_hierarchical_modeling

    Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. [1] The sub-models combine to form the hierarchical model, and Bayes' theorem is used to integrate them with the observed data and account for all the ...

  8. One-shot learning (computer vision) - Wikipedia

    en.wikipedia.org/wiki/One-shot_learning...

    The Bayesian one-shot learning algorithm represents the foreground and background of images as parametrized by a mixture of constellation models. [12] During the learning phase, the parameters of these models are learned using a conjugate density parameter posterior and Variational Bayesian Expectation–Maximization (VBEM). [13]

  9. Probabilistic neural network - Wikipedia

    en.wikipedia.org/wiki/Probabilistic_neural_network

    A probabilistic neural network (PNN) [1] is a feedforward neural network, which is widely used in classification and pattern recognition problems.In the PNN algorithm, the parent probability distribution function (PDF) of each class is approximated by a Parzen window and a non-parametric function.