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 or discriminative.
Classification algorithms (supervised algorithms predicting categorical labels)
In Classification algorithm for pattern recognition, it have two kinds of alogrithm: parametric and non-parametric.
Parametric:
- Linear discriminant analysis
- 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:
- Decision trees, decision lists
- Kernel estimation and K-nearest-neighbor algorithms
- Naive Bayes classifier
- Neural networks (multi-layer perceptrons)
- Perceptrons
- Support vector machines (svm)
- Gene expression programming
Clustering algorithms (unsupervised algorithms predicting categorical labels)
Clustering algorithm for pattern recognition have some algorithm. They are:
- Categorical mixture models
- Deep learning methods
- Hierarchical clustering (agglomerative or divisive)
- K-means clustering
- Correlation clustering
- Kernel principal component analysis (Kernel PCA)
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