Pattern recognition
Pattern recognition is a branch of machine learning that focuses on detecting and classifying patterns and ordered structures in input data. Pattern recognition is linked closely to machine learning but pattern recognition is concerned with finding, extracting, and visualizing patterns from input data while the focus lies on building models from data by learning algorithms in machine learning.

A major application of pattern recognition is classification, which attempts to assign each input to one of a given set of classes. In general, pattern recognition systems consist of the components of pre-processing, feature extraction, classification, and post-processing. The pre-processor performs operations on input sensor data such as noise reduction, filtering, and segmentation to isolate the pattern from background and to enhance it. In feature extraction, problem-specific processing is applied to extract salient features that are useful for classification. Efforts are taken in the feature extraction step as the recognition performance relies on whether or not the features are effective. That is why the step is called “feature engineering”. The problem-specific processing requires dimensional reduction, transformations, and spatiotemporal computation. The classifier, constructed based on machine learning algorithms, then uses the extracted features to assign the input to a category.
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