Pattern recognition trough AI

One of the greatest strengths of Artificial Intelligence systems is their ability to find rules or patterns in big data, pictures, sounds and much more.

Many functions of intelligent information systems are based on methods of pattern recognition: support for diagnoses in the area of medicine, voice recognition with assistance systems and translation tools, object detection in camera images and videos or also forecasting of stock prices. All of these applications involve identifying patterns – or rules – in large volumes of data. It is immaterial whether this data relates to information stored in a database or to pixels in an image or the operating data of a machine. Such identification of patterns was either not possible at all with classic computer systems or required lengthy calculation times of up to several days.

Classifying data in seconds

Developments in the area of neural networks and machine learning have led to the emergence of solutions today in which even complex input data can be matched and classified within minutes or even seconds with trained features. A distinction is made here between two fundamental methods: supervised and unsupervised classification.

With supervised classification of input data in pattern recognition, the system is “fed” training data, with the data with the correct result being labelled accordingly. The correct response must therefore be available during the training phase and the pattern recognition algorithm has to fill the gap between input and output. This form of supervised pattern recognition is used with machine vision for object detection or for facial recognition for example.

In the case of unsupervised learning, the training data is not labelled, which means that the possible results are not known. The pattern-recognition algorithm therefore cannot be trained by providing it with the results it is to arrive at. Algorithms are used more so, which explore the structure of the data and derive meaningful information from it. To stay with the example of machine vision: the techniques of unsupervised pattern recognition are used for object detection, among other things. Unsupervised methods are essentially also used for data mining, thus for detecting contents in large data volumes based on visibly emerging structures.

Finding structures in big data

A number of different methods are in turn used in this type of big data analysis. One such example is association pattern analysis. A set of training data is searched through in this case for combinations of individual facts or events, which occur significantly often or significantly rarely together in the data. Another example in this context is what is known as sequential pattern mining. A set of training data is searched through to identify time-ordered sequences that occur conspicuously often or rarely in succession in the data. The result of the different mining methods is a collection of patterns or rules, which can be applied to future data sets to discover whether one or more rules occur in these data sets. The rules can be integrated in operative software programs in order to develop early warning concepts, for example, or to predict when maintenance is due.

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