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Neural networks and pattern recognition
One of the most common applications of machine learning is pattern recognition. Computers that use well-trained algorithms recognize animals in photos, anomalies in stock fluctuations, and signs of cancer in mammograms much better than humans. Let us find out what lies behind this complex process.
What is pattern recognition?
Pattern recognition is the process of recognizing regularities in data by a machine that uses machine learning algorithms. In the heart of the process lies the classification of events based on statistical information, historical data, or the machine’s memory.
What would Netflix do?
A pattern is a regularity in the world or in abstract notions. If we talk about books or movies, a description of a genre would be a pattern. If a person keeps watching black comedies, Netflix wouldn’t recommend them heartbreaking romantic comedies or would it?!
Useful articles on pattern recognition.
The neural approach to pattern recognition (acm.org)
Additional Information
For the machine to search for patterns in data, it should be preprocessed and converted into a form that a computer can understand. Then, the researcher can use classification, regression, or clustering algorithms depending on the information available about the problem to get valuable results.
- Classification. In classification, the algorithm assigns labels to data based on the predefined features. This is an example of supervised learning. More about classification algorithms you can read in our blog.
- Clustering. An algorithm splits data into a number of clusters based on the similarity of features. This is an example of unsupervised learning.
- Regression. Regression algorithms try to find a relationship between variables and predict unknown dependent variables based on known data. It is based on supervised learning.
Source and recommended readnig: Machine Learning: Pattern Recognition (serokell.io)