What is the difference between classification and pattern recognition?

Question

Could someone explain the difference between classification and pattern recognition as simply as possible or direct me to the right place?!

Answer 1

Pattern recognition is a generic term for the ability to recognize regularities or patterns in data. A more generic one is machine learning. Classification is an example of pattern recognition, where a model devides the data into classes.

More specific from left to right: Machine Learning > Pattern Recognition > Classification > Linear Classification > SVM

Answer 2

There are many types of Pattern Recognition algorithms, and Classification algorithms is one among them, i.e. not all pattern recognition algorithms are classifier algorithms.

To qualify as a classifier, an algorithm needs to map an input data point to a category among a set of categories (or labels, or classes)

As an example of a pattern recognition algorithm that isn't a classifier, consider the k-means algorithm, which is a clustering algorithm. When the k-means algorithm runs it finds patterns in your data and try to splits into distinct clusters.

_{Image from Wikipedia page of k-means algorithm}

If you wish to attach a label to an input to classify it into one of the clusters (e.g. returned by the k-means), you may e.g. use a classifier algorithm like k-nearest neighbors (k-NN), which takes an input and as output predicts the cluster to which it is classified.