Pattern Recognition | Introduction

Pattern is everything around in this digital world. A pattern can either be seen physically or it can be observed mathematically by applying algorithms.
 Example: The colors on the clothes, speech pattern etc. In computer science, a pattern is represented using vector features values.

What is Pattern Recognition ?

The process of recognition of patterns based on existing data, gained knowledge or on statistical information and/or its representation by using machine learning algorithm is Pattern Recognition.

And using machine learning and Artificial Intelligence(AI) it can be used to provide smart solutions to various existing problems.

We provide a reasonable answer for all possible data and to classify input data into objects or classes based on certain features. A “most likely” matching is performed between various data samples and their key features are matched and recognized.

The best feature of the pattern recognition is the vast application potential it holds.

Clustering generated a partition of the data which helps decision making, the specific decision making activity of interest to us. Clustering is used in an unsupervised learning.

Features may be represented as continuous, discrete or discrete binary variables. A feature is a function of one or more measurements, computed so that it quantifies some significant characteristics of the object.
 Example: Consider our face then eyes, ears, nose etc are features of the face.

A set of features that are taken together, forms the features vector.

Pattern recognition possesses the following features:

Pattern recognition system recognizes familiar pattern quickly and accurate
Recognize and classify unfamiliar objects
Accurately recognize shapes and objects from different angles
Identify patterns and objects even when partly hidden
Recognize patterns quickly with ease, and with automaticity.
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