Features (pattern recognition)

# Features (pattern recognition)

Discussion
 Ask a question about 'Features (pattern recognition)' Start a new discussion about 'Features (pattern recognition)' Answer questions from other users Full Discussion Forum

Encyclopedia
In pattern recognition
Pattern recognition
In machine learning, pattern recognition is the assignment of some sort of output value to a given input value , according to some specific algorithm. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes...

, features are the individual measurable heuristic
Heuristic
Heuristic refers to experience-based techniques for problem solving, learning, and discovery. Heuristic methods are used to speed up the process of finding a satisfactory solution, where an exhaustive search is impractical...

properties of the phenomena being observed. Choosing discriminating and independent features is key to any pattern recognition algorithm being successful in classification. Features are usually numeric, but structural features such as strings
String (computer science)
In formal languages, which are used in mathematical logic and theoretical computer science, a string is a finite sequence of symbols that are chosen from a set or alphabet....

and graphs
Graph (mathematics)
In mathematics, a graph is an abstract representation of a set of objects where some pairs of the objects are connected by links. The interconnected objects are represented by mathematical abstractions called vertices, and the links that connect some pairs of vertices are called edges...

are used in syntactic pattern recognition
Syntactic pattern recognition
Syntactic pattern recognition or structural pattern recognition is a form of pattern recognition, in which each object can be represented by a variable-cardinality set of symbolic, nominal features...

.

## Classification

While different areas of pattern recognition obviously have different features, once the features are decided, they are classified by a much smaller set of algorithms. These include nearest neighbor classification
K-nearest neighbor algorithm
In pattern recognition, the k-nearest neighbor algorithm is a method for classifying objects based on closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until...

in multiple dimensions, neural networks
Neural Networks
Neural Networks is the official journal of the three oldest societies dedicated to research in neural networks: International Neural Network Society, European Neural Network Society and Japanese Neural Network Society, published by Elsevier...

or statistical techniques such as Bayesian approaches
Bayesian inference
In statistics, Bayesian inference is a method of statistical inference. It is often used in science and engineering to determine model parameters, make predictions about unknown variables, and to perform model selection...

.

## Examples

In character recognition, features may include horizontal and vertical profiles, number of internal holes, stroke detection and many others.

In speech recognition
Speech recognition
Speech recognition converts spoken words to text. The term "voice recognition" is sometimes used to refer to recognition systems that must be trained to a particular speaker—as is the case for most desktop recognition software...

, features for recognizing phonemes can include noise ratios, length of sounds, relative power, filter matches and many others.

In spam
Spam (electronic)
Spam is the use of electronic messaging systems to send unsolicited bulk messages indiscriminately...

detection algorithms, features may include whether certain email headers are present or absent, whether they are well formed, what language the email appears to be, the grammatical correctness of the text, markovian frequency analysis and many others.

In all these cases, and many others, extracting features
Feature extraction
In pattern recognition and in image processing, feature extraction is a special form of dimensionality reduction.When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant then the input data will be transformed into a reduced representation...

that are measurable by a computer is an art, and with the exception of some neural networking and genetic techniques that automatically intuit "features", hand selection of good features forms the basis of almost all classification algorithms.