What is Pattern Recognition?
AI and ML projects have a specialized key feature known as Pattern Recognition. It is used to recognize patterns with the help of machine learning algorithms. It is the process of classifying the data according to the model that is gained by training data that helps in detecting patterns and characteristics from the patterns. Pattern recognition is used in detecting various categories to extract information from a particular data. Examples are voice recognition, object detection, and so on.
Pattern Recognition is the study of a field about automating the discovery of regularities in data using computer algorithms for taking up actions like classifying the data into various categories. In the machine learning process, pattern recognition is the process of assigning a label to a given input data.
Pattern recognition algorithms are aiming to offer a reasonable solution for all possible data inputs by performing ‘most likely’ matching of the input data by taking their statistical variation. It is commonly categorized as per the type of learning procedure used to create the output value.
In this blog, we are about to explain how pattern recognition is used in the machine learning process for finding the common patterns for various applications like big data processing, computer vision, signal processing, processing power, image analysis, and so on.
Features of Pattern Recognition
Following are the key features of pattern recognition
- It learns from the data
- It automatically recognizes patterns even if the patterns are partially visible
- It can be used in recognizing familiar patterns.
- It can be recognized from various angles and shapes.
Training Models in Pattern Recognition
Initially, the gathered data must be segregated into two sets. They are a training set and a testing set. Learning from the data explains how the predictions of the system are relying on the data that are provided to decide which algorithm will be appropriate for them. Here, we can use training data to train an algorithm and testing data to test the model as data is divided into two categories.
Now, we divide data into two sets in which we can use 70% of data for training an algorithm for deriving the important insights and patterns from the gathered data to create the model. Then, 30% of the data can be used to test the model for verifying the performance of the model to find how accurately is the model predicting the outcomes.
Applications of Pattern Recognition
Computer Vision: Pattern Recognition is used to recognize objects in the images. It is used to extract a particular pattern from videos or images for implementation in face recognition, farming tech, etc.
Civil Administration: Pattern Recognition is used in surveillance and traffic analysis control systems for identifying objects like bikes or cars.
Engineering: Pattern Recognition helps speech recognition systems that can be implemented in Alexa, Google Now, or Siri.
Geology: Pattern Recognition is used for rock recognition processes that help geologists to detect rocks and hills.
Fingerprint Scanning: The pattern recognition system is used in fingerprint scanning systems for identifying a person to track attendance in organizations.
Advantages of Pattern Recognition
Following are the advantages of the pattern recognition process
- Easy to interpret DNA sequences
- Extensive support for medical and robotics fields.
- Easy to implement for solving classification problems
- High support for biometric detection processes
- Easy to recognize objects that are from various angles and sizes
How Pattern Recognition Differs from Machine Learning?
Machine Learning is a concept that learns from the data without being explicitly programmed. It will be iterative and accurate in performing repetitive tasks. Pattern recognition plays an important role in machine learning processes by supporting what it actually wants to achieve. ML models will be developed to understand patterns with statistical analysis that can be classified data further. Thus, the outcome will have a probable value or rely on the data occurrence.
Pattern recognition is an important feature of machine learning and it is widely used in various applications. It brings a solution to data analytics by recognizing patterns easily from the raw data that are collected from various sources. Explore how pattern recognition can be implemented in the machine learning process by learning in our Machine Learning Training in Chennai at Softlogic.