In machine learning, pattern recognition is the assignment of a label to a given input value. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is “spam” or “non-spam”). However, pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression, which assigns a real-valued output to each input; sequence labeling, which assigns a class to each member of a sequence of values (for example, part of speech tagging, which assigns a part of speech to each word in an input sentence); and parsing, which assigns a parse tree to an input sentence, describing the syntactic structure of the sentence.

Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to do “fuzzy” matching of inputs. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors. In contrast to pattern recognition, pattern matching is generally not considered a type of machine learning, although pattern-matching algorithms (especially with fairly general, carefully tailored patterns) can sometimes succeed in providing similar-quality output to the sort provided by pattern-recognition algorithms.

**Pattern Analysis Computation Methods:**

- Ridge regression
- Regularized Fisher discriminant
- Regularized kernel Fisher discriminant
- Maximizing variance
- Maximizing covariance
- Canonical correlation analysis
- Kernel CCA
- Regularized CCA
- Kernel regularized CCA
- Smallest enclosing hyper sphere
- Soft minimal hyper sphere
- nu-soft minimal hyper sphere
- Hard margin SVM
- 1-norm soft margin SVM
- 2-norm soft margin SVM
- Ridge regression optimization
- Quadratic e-insensitive
- Linear e-insensitive SVR
- nu-SVR
- Soft ranking
- Cluster quality
- Cluster optimization strategy
- Multiclass clustering
- Relaxed multiclass clustering
- Visualization quality

**Pattern Analysis Algorithms:**

- Normalization
- Centering data
- Simple novelty detection
- Parzen based classifier
- Cholesky decomposition or dual Gram�Schmidt
- Standardizing data
- Kernel Fisher discriminant
- Primal PCA
- Kernel PCA
- Whitening
- Primal CCA
- Kernel CCA
- Principal components regression
- PLS feature extraction
- Primal PLS
- Kernel PLS
- Smallest hyper sphere enclosing data
- Soft hyper sphere minimization
- nu-soft minimal hyper sphere
- Hard margin SVM
- Alternative hard margin SVM
- 1-norm soft margin SVM
- nu-SVM
- 2-norm soft margin SVM
- Kernel ridge regression
- 2-norm SVR
- 1-norm SVR
- nu-support vector regression
- Kernel perceptron
- Kernel adatron
- On-line SVR
- nu-ranking
- On-line ranking
- Kernel k-means
- MDS for kernel-embedded data
- Data visualization

Source: http://www.kernel-methods.net/algos.html

Keywords: Data Mining, Machine Learning, Algorithms