Pattern Recognition Definition

Pattern Recognition — is all about a data analysis “rocket science” method referring to collecting and classifying data that is inputted into categories. There are two primary methods — supervised (which stands for using a computer algorithm to define data) and unsupervised (refers to no human guiding for a machine learning algorithm and process). Within Pattern Recognition you are able to apply it for any type of data from images and text to video and audio.

As for the key steps we can define three of them — analyzing defining and data comparing. First of all you recognize what data has to be stored then algorithms start operating processing through the inputted information according to the machine learning algorithm you choose. Then all the data will be classified due to the chosen method.

Types of pattern recognition: visual, audio, text

Pattern recognition if we are talking in terms of visual, audio, text is essential part of cognitive processing that encompasses various modalities. In other words, within this method you are able to identify patterns with the usage of images or visual stimuli — faces and objects recognition, spatial configuration that help with computer vision and human perception as a rocket science part of identifying.

As we have said, there are three crucial categories of pattern recognition — audio stands for interpreting sound signals which helps us to perceive voices, speech, music (which is essential for UX design). As for the text perception, generally, it’s all about what content you post within your website or mobile app.