Editing List of algorithms (section)

===Machine learning and statistical classification===
{{main|List of machine learning algorithms}}
{{further|Machine learning|Statistical classification}}
* [[Almeida–Pineda recurrent backpropagation]]: Adjust a matrix of synaptic weights to generate desired outputs given its inputs
* [[ALOPEX]]: a correlation-based [[Machine learning|machine-learning algorithm]]
* [[Association rule learning]]: discover interesting relations between variables, used in [[data mining]]
** [[Apriori algorithm]]
** [[Eclat algorithm]]
** [[Association rule learning#FP-growth algorithm|FP-growth algorithm]]
** [[One-attribute rule]]
** [[Association rule learning#Zero-attribute rule|Zero-attribute rule]]
* [[Boosting (meta-algorithm)]]: Use many weak learners to boost effectiveness
** [[AdaBoost]]: adaptive boosting
** [[BrownBoost]]: a boosting algorithm that may be robust to noisy datasets
** [[LogitBoost]]: [[logistic regression]] boosting
** [[LPBoost]]: [[linear programming]] boosting
* [[Bootstrap aggregating]] (bagging): technique to improve stability and classification accuracy
* [[Computer Vision]]
** [[Grabcut]] based on [[Graph cuts in computer vision|Graph cuts]]
* [[Decision tree learning|Decision Trees]]
** [[C4.5 algorithm]]: an extension to ID3
** [[ID3 algorithm]] (Iterative Dichotomiser 3): use heuristic to generate small decision trees
* [[Cluster analysis|Clustering]]: a class of [[unsupervised learning]] algorithms for grouping and bucketing related input vector
* [[k-nearest neighbors]] (k-NN): a non-parametric method for classifying objects based on closest training examples in the [[feature space]]
* [[Linde–Buzo–Gray algorithm]]: a vector quantization algorithm used to derive a good codebook
* [[Locality-sensitive hashing]] (LSH): a method of performing probabilistic dimension reduction of high-dimensional data
* [[Artificial neural network|Neural Network]]
** [[Backpropagation]]: a [[supervised learning]] method which requires a teacher that knows, or can calculate, the desired output for any given input
** [[Hopfield net]]: a [[Recurrent neural network]] in which all connections are symmetric
** [[Perceptron]]: the simplest kind of feedforward neural network: a [[linear classifier]].
** [[Pulse-coupled neural networks]] (PCNN): [[Artificial neural network|Neural models]] proposed by modeling a cat's [[visual cortex]] and developed for high-performance [[Bionics|biomimetic]] image processing.
** [[Radial basis function network]]: an artificial neural network that uses radial [[basis function]]s as activation functions
** [[Self-organizing map]]: an unsupervised network that produces a low-dimensional representation of the input space of the training samples
* [[Random forest]]: classify using many decision trees
* [[Reinforcement learning]]:
** [[Q-learning]]: learns an action-value function that gives the expected utility of taking a given action in a given state and following a fixed policy thereafter
** [[State–action–reward–state–action|State–Action–Reward–State–Action]] (SARSA): learn a [[Markov decision process]] policy
** [[Temporal difference learning]]
* [[relevance vector machine|Relevance-Vector Machine]] (RVM): similar to SVM, but provides probabilistic classification
* [[Supervised learning]]: Learning by examples (labelled data-set split into training-set and test-set)
* [[Support vector machine|Support Vector Machine]] (SVM): a set of methods which divide multidimensional data by finding a dividing hyperplane with the maximum margin between the two sets
** [[Structured SVM]]: allows training of a classifier for general structured output labels.
* [[Winnow algorithm]]: related to the perceptron, but uses a [[Multiplicative Weight Update Method|multiplicative weight-update scheme]]