what is pattern recognition?

Answer 1

Pattern recognition is the method computers use to determine parity with a substance or event. In layman's terms it is used by computers to determine if something is happening or what something is. For example, an apple looks a certain way; it has a stem, a concave top and bottom and is otherwise round. These characteristics exhibit "patterns" that are common to all apples. Other fruit, when compared to an apple, will fail the "pattern test" when scanned by a computer.

Although a simple example, pattern matching can be applied to just about any identification technique. Everyone knows about fingerprints, but what about other, more access able ways to identify somebody. A person's head, for example, can be scanned and patterned matched to identify that person's head throughout their lifetime. Extrapolating this concept is frightening. Think of digital cameras placed in various places throughout the world; trees, telephone poles, airports, train stations, even homes and bedrooms. These cameras will be capable of tracking ones movements at will and without consent. Scary but true. This is the logical direction of pattern matching. Cool technology, scary application.

Answer 2

An element of "vision". For those working on getting
computers to "see" it is a part of solving that puzzle.
If you take the image (bits) coming from a camera,
you may want a computer to be able to identify things
in the image. It turns out this is hard. If you simplify
the image to just a 2-D black and white one, you will
find patterns that may be easier to recognize, such
as letters or simplified artistic shapes. Character
recognition is a subset of this (see "OCR" optical
character recognition).

Answer 3

Pattern recognition is a field within the area of machine learning. Alternatively, it can be defined as

"the act of taking in raw data and taking an action based on the category of the data" [1].

As such, it is a collection of methods for supervised learning.

Pattern recognition aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space.

A complete pattern recognition system consists of a sensor that gathers the observations to be classified or described; a feature extraction mechanism that computes numeric or symbolic information from the observations; and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features.

The classification or description scheme is usually based on the availability of a set of patterns that have already been classified or described. This set of patterns is termed the training set and the resulting learning strategy is characterised as supervised learning. Learning can also be unsupervised, in the sense that the system is not given an a priori labelling of patterns, instead it establishes the classes itself based on the statistical regularities of the patterns.

The classification or description scheme usually uses one of the following approaches: statistical (or decision theoretic), syntactic (or structural). Statistical pattern recognition is based on statistical characterisations of patterns, assuming that the patterns are generated by a probabilistic system. Structural pattern recognition is based on the structural interrelationships of features. A wide range of algorithms can be applied for pattern recognition, from very simple Bayesian classifiers to much more powerful neural networks.

Holographic associative memory is another type of pattern matching scheme where a target small patterns can be searched from a large set of learned patterns based on cognitive meta-weight.

Typical applications are automatic speech recognition, classification of text into several categories (e.g. spam/non-spam email messages), the automatic recognition of handwritten postal codes on postal envelopes, or the automatic recognition of images of human faces. The last two examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems.

Pattern recognition is more complex when templates are used to generate variants. For example, in English, sentences often follow the "N-VP" (noun - verb phrase) pattern, but some knowledge of the English language is required to detect the pattern. Pattern recognition is studied in many fields, including psychology, ethology, and computer science.

Answer 4

pareidolia = the imagined perception of a pattern or meaning where it does not actually exist, as in considering the moon to have human features. In my culture's religion (Armenian), before becoming christian, it was believed that when the wind blew past the ouri tree, it made a woooo woooo sound and it was believed to be god's breath. They believed in many gods. I'm sure you have heard that wooo wooo sound when a very strong wind blows. People came up with explanations for the natural phenomenon they observed but missed the ones they could not observe.

Answer 5

Once you understand what pattern recognition is you should come to realise it is what intelligence, any intelligence, simplifies to.

Answer 6

may be the dna pattern recognition used in dna finger printing

Answer 7

not enough info. So would have to say the act of recognizing patterns.

Answer 8

the act of taking in raw data and taking an action based on the category of the data

Answer 9

ummm i dnt know