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A model of the conditional distribution (=) is a model of the distribution of each label, and a model of the joint distribution is equivalent to a model of the distribution of label values (), together with the distribution of observations given a label, (); symbolically, (,) = ().
The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael Kearns , Yishay Mansour , Dana Ron , Ronitt Rubinfeld , Robert Schapire and Linda Sellie in 1994 [ 1 ] and it was inspired from the PAC-framework introduced by Leslie Valiant .
A machine learning model is a type of mathematical model that, ... Given a set of observed points, or input–output examples, the distribution of the (unobserved ...
Centroid model s: for example, the k-means algorithm represents each cluster by a single mean vector. Distribution model s: clusters are modeled using statistical distributions, such as multivariate normal distributions used by the expectation-maximization algorithm.
In distribution regression, the goal is to regress from probability distributions to reals (or vectors). Many important machine learning and statistical tasks fit into this framework, including multi-instance learning, and point estimation problems without analytical solution (such as hyperparameter or entropy estimation).
Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units) A restricted Boltzmann machine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
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.
MDL applies in machine learning when algorithms (machines) generate descriptions. Learning occurs when an algorithm generates a shorter description of the same data set. The theoretic minimum description length of a data set, called its Kolmogorov complexity, cannot, however, be computed.