In today’s world, a lot of different type of data is flowing across systems in order to categorise the data we cannot use traditional programming which has rules that can check some conditions and classify data. The solution to this problem is Machine Learning, with the help of it we can create a model which can classify different patterns from data. One of the applications of this is the classification of spam or non-spam data.
In Machine Learning we cannot expect a model to be 100% accurate but the predictions should be as close as possible so that it can be categorized in a particular category. In Machine Learning the model is created based on some algorithms which learn from the data provided to make predictions. The model builds on statistics. Machine learning takes some data to analyze it and automatically create some model which can predict things. In order to get good predictions from a model, we need to provide data that has different characteristics so that the algorithms will understand different patterns which may exist in a given problem.
Patterns are recognised by the help of algorithms used in Machine Learning. Recognising patterns is the process of classifying the data based on the model that is created by training data, which then detects patterns and characteristics from the patterns. Pattern recognition is the process which can detect different categories and get information about particular data. Some of the applications of patterns recognition are voice recognition, weather forecast, object detection in images, etc.
Features of Pattern Recognition:
- Pattern recognition learns from the data.
- Automatically recognise patterns even when partially visible.
- Should be able to recognise patterns which are familiar.
- The pattern should be recognised from different angles and shapes.
Training and Learning Models in Pattern Recognition
Firstly the data should be divided into to set i.e training and testing set. Learning from the data can tell how the predictions of the system are depending on the data provided as well which algorithm suits well for specific data, this is a very important phase. As data is divided into two categories we can use training data to train an algorithm and testing data is used to test model, as already said the data should be diverse training and testing data should be different.
So we divide data into two sets normally we divide data in which 70% of data is used for training the model, algorithms extract the important patterns from the provided data and creates a model. Testing set contains 30% of whole data and it is then used to verify the performance of the model i.e how accurately is the model predicting the results.
Applications of Pattern Recognition
Computer vision: Objects in images can be recognised with the help of pattern recognition which can extract certain patterns from image or video which can be used in face recognition, farming tech, etc.
Civil administration: surveillance and traffic analysis systems to identify objects such as a car.
Engineering: Speech recognition is widely used in systems such as Alexa, Siri, and Google Now.
Geology: rocks recognition, it helps geologist to detect rocks.
Speech recognition: In speech recognition, words are treated as a pattern and is widely used in the speech recognition algorithm.
Fingerprint scanning: In fingerprint recognition, pattern recognition is widely used to identify a person one of the application to track attendance in organisations.
Advantages of Pattern Recognition
- DNA sequences can be interpreted
- Extensively applied in the medical field and robotics.
- Classification problems can be solved using pattern recognition.
- Biometric detection
- Can recognise a particular object from different angles.
Difference Between Machine Learning and Pattern Recognition
ML is an aspect which learns from the data without explicitly programmed, which may be iterative in nature and becomes accurate as it keeps performing tasks.
ML is a form of pattern recognition which is basically the idea of training machines to recognise patterns and apply them to practical problems.
ML is a feature which can learn from data and iteratively keep updating itself to perform better but, Pattern recognition does not learn problems but, it can be coded to learn patterns.
pattern recognition is defined as data classification based on the statistical information gained from patterns.
Pattern recognition plays an important role in the task which machine learning is trying to achieve. Similarly, as humans learn by recognising patterns. Patterns vary from visual patterns, sound patterns, signals, weather data, etc
ML model can be developed to understand patterns using statistical analysis which can classify data further. The results might be a probable value or depend on the likelihood of the occurrence of data.
In this article, we took a look at what is machine learning and pattern recognition, how they work together in order to create an accurate and efficient model. We explored different features of pattern recognition. Also, how the data is divided into a training set and testing set and how that can be used to create an efficient model which could provide accurate predictions. What are the applications of them and how they differ from each other is discussed in brief.