The practice of recognizing patterns and classifying data accordingly has prevailed for a long time, and human beings have taken long strides in this field. Classifying information observed from nature was practiced even by the Stone Age man.
He used different patterns to identify plants, animals, and other elements of nature. The difference between those times and that of today is the availability of high tech instruments. Also, today it is possible to store huge amounts of data in computers.
The field of research activity, in which observations being made are classified and described, is known as pattern recognition. It is one of the applications of artificial intelligence. If statistical information obtained from patterns is used in their classification, the method is known as statistical pattern recognition.
Pattern Recognition is sub-divided into other disciplines such as feature extraction, discriminant analysis, cluster analysis, and error estimation. This type of syntactical methodology carries out grammatical parsing and inference.
Its methods are often used in identifying data that is very complicated. Therefore, this identification system can fall in the group of algorithmic modeling.
Prior knowledge of the patterns, instead of just obtaining statistical data, should also prove to be useful in classifying. There are three steps involved in this system. First is the reception of observations or data by means of sensors. These receptors/sensors gather information to be classified.
Computation of numerical data and symbolic information is carried out by means of a mechanism called feature extraction. The information that is gathered and then extracted in these two steps is finally classified.
Pattern Recognition and Machine Learning
As mentioned, it is one of the branches of Artificial Intelligence. In different artificial intelligence programs, machine learning helps in carrying out pattern recognition. One of the applications of using pattern recognition and machine learning is statistical data mining.
In the process of machine learning, a computer is provided instructions as to how a particular task should be carried out. The process is implemented in two different ways, i.e., through supervised and unsupervised learning.
▶ Supervised Learning: In this, the computer to be taught is provided with pattern recognition algorithms. Different examples are presented to the computer. The examples show how the process of task completion is executed. It also gives information about the product. Throughout the process of training the computer, feedback is also provided.
▶ Unsupervised Learning: In this, the computer doesn't get any feedback or guidance while learning. No guidelines are provided either. It means that unlike supervised learning, patterns are not labeled or classified beforehand. The process of classifying information created by the artificial intelligence program, thus, needs to be very efficient.
Applications such as Computer-Aided Diagnosis (CAD) make use of pattern recognition software. It is also used in classifying a particular text in different categories like speech recognition, recognizing handwriting, industrial inspection, person identification, etc.
It is used in image analysis. One of the important image analysis tools used by computers is the neural networks. The neural network and other tools like edge detectors, which are based on the model of human visual perception can be used in the process of image analysis.
Different types of pattern recognition tests can be used in measuring the aptitude of a person. One gets an indication of IQ with such tests. The questions presented in such tests require us to recognize the pattern hidden in the given design, set of numbers, etc.