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Suppose a pair (,) takes values in {,, …,}, where is the class label of an element whose features are given by .Assume that the conditional distribution of X, given that the label Y takes the value r is given by (=) =,, …, where "" means "is distributed as", and where denotes a probability distribution.
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.
Automatically learning the graph structure of a Bayesian network (BN) is a challenge pursued within machine learning. The basic idea goes back to a recovery algorithm developed by Rebane and Pearl [ 7 ] and rests on the distinction between the three possible patterns allowed in a 3-node DAG:
In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant . [ 1 ]
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]
In mathematics, a Relevance Vector Machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic classification. [1] A greedy optimisation procedure and thus fast version were subsequently developed.
In practice, for almost all complex Bayesian models used in machine learning, the posterior distribution (,) is not obtained in a closed form distribution, mainly because the parameter space for can be very high, or the Bayesian model retains certain hierarchical structure formulated from the observations and parameter . In such situations, we ...
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 .