Editing Supervised learning (section)

==Approaches and algorithms==

* Analytical learning
* [[Artificial neural network]]
* [[Backpropagation]]
* [[Boosting (meta-algorithm)]]
* [[Bayesian statistics]]
* [[Case-based reasoning]]
* [[Decision tree learning]]
* [[Inductive logic programming]]
* [[Gaussian process regression]]
* [[Genetic programming]]
* [[Group method of data handling]]
* [[Variable kernel density estimation#Use for statistical classification|Kernel estimators]]
* [[Learning automaton|Learning automata]]
* [[Learning classifier system]]s
* [[Learning vector quantization]]
* [[Minimum message length]] ([[decision tree]]s, decision graphs, etc.)
* [[Multilinear subspace learning]]
* [[Naive Bayes classifier]]
* [[Maximum entropy classifier]]
* [[Conditional random field]]
* [[Nearest neighbor (pattern recognition)|Nearest neighbor algorithm]]
* [[Probably approximately correct learning]] (PAC) learning
* [[Ripple down rules]], a knowledge acquisition methodology
* Symbolic machine learning algorithms
* Subsymbolic machine learning algorithms
* [[Support vector machine]]s
* Minimum complexity machines (MCM)
* [[Random forest]]s
* [[Ensembles of classifiers]]
* [[Ordinal classification]]
* [[Data pre-processing]]
* Handling imbalanced datasets
* [[Statistical relational learning]]
* [[Proaftn]], a multicriteria classification algorithm