Editing Pattern recognition (section)

==Algorithms==
Algorithms for pattern recognition depend on the type of label output, on whether learning is supervised or unsupervised, and on whether the algorithm is statistical or non-statistical in nature. Statistical algorithms can further be categorized as [[generative model|generative]] or [[discriminative model|discriminative]].

===Classification methods (methods predicting categorical labels)===
{{Main|Statistical classification}}

Parametric:<ref>Assuming known distributional shape of feature distributions per class, such as the [[Gaussian distribution|Gaussian]] shape.</ref>
*[[Linear discriminant analysis]]
*[[Quadratic classifier|Quadratic discriminant analysis]]
*[[Maximum entropy classifier]] (aka [[logistic regression]], [[multinomial logistic regression]]): Note that logistic regression is an algorithm for classification, despite its name. (The name comes from the fact that logistic regression uses an extension of a linear regression model to model the probability of an input being in a particular class.)
Nonparametric:<ref>No distributional assumption regarding shape of feature distributions per class.</ref>
*[[Decision tree]]s, [[decision list]]s
*[[Variable kernel density estimation#Use for statistical classification|Kernel estimation]] and [[K-nearest-neighbor]] algorithms
*[[Naive Bayes classifier]]
*[[Artificial neural network|Neural networks]] (multi-layer perceptrons)
*[[Perceptron]]s
*[[Support vector machine]]s
*[[Gene expression programming]]

===Clustering methods (methods for classifying and predicting categorical labels)===
{{Main|Cluster analysis}}

*Categorical [[mixture model]]s
*[[Hierarchical clustering]] (agglomerative or divisive)
*[[K-means clustering]]
*[[Correlation clustering]]<!-- not an algorithm -->
*[[Kernel principal component analysis]] (Kernel PCA)<!-- but not PCA? -->

===Ensemble learning algorithms (supervised meta-algorithms for combining multiple learning algorithms together)===
{{Main|Ensemble learning}}

*[[Boosting (meta-algorithm)]]
*[[Bootstrap aggregating]] ("bagging")
*[[Ensemble averaging]]
*[[Mixture of experts]], [[hierarchical mixture of experts]]

===General methods for predicting arbitrarily-structured (sets of) labels===
*[[Bayesian network]]s
*[[Markov random field]]s

===Multilinear subspace learning algorithms (predicting labels of multidimensional data using tensor representations)===
Unsupervised:
*[[Multilinear principal component analysis]] (MPCA)

===Real-valued sequence labeling methods (predicting sequences of real-valued labels)===
{{Main|sequence labeling}}

*[[Kalman filter]]s
*[[Particle filter]]s

===Regression methods (predicting real-valued labels)===
{{Main|Regression analysis}}

*[[Gaussian process regression]] (kriging)
*[[Linear regression]] and extensions
*[[Independent component analysis]] (ICA)
*[[Principal components analysis]] (PCA)

===Sequence labeling methods (predicting sequences of categorical labels)===
*[[Conditional random field]]s (CRFs)
*[[Hidden Markov model]]s (HMMs)
*[[Maximum entropy Markov model]]s (MEMMs)
*[[Recurrent neural networks]] (RNNs)
*[[Dynamic time warping]] (DTW)


{{cleanup list|date=May 2014}}