Patterns are everywhere. It belongs to every aspect of our daily lives. Starting from the design and color of our clothes to using intelligent voice assistants, everything involves some kind of pattern. When we say that everything consists of a pattern or everything has a pattern, the common question that comes up to our minds is, what is a pattern? How can we say that it constitutes almost everything and anything surrounding us? How can it be implemented in the technologies that we use every day?
Well, the answer to all these questions is one of the simplest things that all of us have probably been doing since childhood. When we were in school, we were often given the task of identifying the missing alphabets to predict which number would come in a sequence next or to join the dots for completing the figure. The prediction of the missing number or alphabet involved analyzing the trend followed by the given numbers or alphabets. This is what pattern recognition in Machine Learning means.
What is Pattern Recognition?
Pattern Recognition is defined as the process of identifying the trends (global or local) in the given pattern. A pattern can be defined as anything that follows a trend and exhibits some kind of regularity. The recognition of patterns can be done physically, mathematically, or by the use of algorithms. When we talk about pattern recognition in machine learning, it indicates the use of powerful algorithms for identifying the regularities in the given data. Pattern recognition is widely used in the new age technical domains like computer vision, speech recognition, face recognition, etc.
Types of Pattern Recognition Algorithms in Machine Learning
1. Supervised Algorithms
The pattern recognition a supervised approach is called classification. These algorithms use a two stage methodology for identifying the patterns. The first stage the development/construction of the model and the second stage involves the prediction for new or unseen objects. The key features involving this concept are listed below.
- Partition the given data into two sets- Training and Test set
- Train the model using a suitable machine learning algorithm such as SVM (Support Vector Machines), decision trees, random forest, etc.
- Training is the process through which the model learns or recognizes the patterns in the given data for making suitable predictions.
- The test set contains already predicted values.
- It is used for validating the predictions made by the training set.
- The model is trained on the training set and tested on the test set.
- The performance of the model is evaluated based on correct predictions made.
- The trained and tested model developed for recognizing patterns using machine learning algorithms is called a classifier.
- This classifier is used to make predictions for unseen data/objects.
2. Unsupervised Algorithms
In contrast to the supervised algorithms for pattern make use of training and testing sets, these algorithms use a group by approach. They observe the patterns in the data and group them based on the similarity in their features such as dimension to make a prediction. Let’s say that we have a basket of different kinds of fruits such as apples, oranges, pears, and cherries. We assume that we do not know the names of the fruits. We keep the data as unlabeled. Now, suppose we encounter a situation where someone comes and tells us to identify a new fruit that was added to the basket. In such a case we make use of a concept called clustering.
- Clustering combines or group items having the same features.
- No previous knowledge is available for identifying a new item.
- They use machine learning algorithms like hierarchical and k-means clustering.
- Based on the features or properties of the new object, it is assigned to a group to make a prediction.
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Tools used for Pattern Recognition in Machine Learning
- Amazon Lex – It is an open-source software/service provided by Amazon for building intelligent conversation agents such as chatbots by using text and speech recognition.
- Google Cloud AutoML – This technology is used for building high-quality machine learning models with minimum requirements. It uses neural networks (RNN -recurrent neural networks) and reinforcement learning as a base for model construction.
- R-Studio – It uses the R programming language for code development. It is an integrated development environment for developing and testing pattern recognition models.
- IBM Watson Studio – IBM Watson Studio is an open-source tool provided by IBM for data analysis and machine learning. It is used for the building and deployment of machine learning models on a desktop.
- Microsoft Azure Machine Learning Studio – Provided by Microsoft, this tool is using a drag and drop concept for building and deployment of the machine learning models. It offers a GUI (Graphical User Interface) based environment for model construction and usage.
Scope of Pattern Recognition in Machine Learning
- Data Mining- It refers to the extraction of useful information from large amounts of data from heterogeneous sources. The meaningful data obtained from data mining techniques are used for prediction making and data analysis.
- Recommender Systems– Most of the websites dedicated to online shopping make use of recommender systems. These systems collect data related to each customer purchase and make suggestions using machine learning algorithms by identifying the trends in the pattern of customer purchase.
- Image processing– Image process is basically of two types – Digital Image processing and Analog image processing. Digital image processing uses intelligent machine learning algorithms for enhancing the quality of the image obtained from distant sources such as satellites.
- Bioinformatics – It is a field of science that uses computation tools and software to make predictions relating to biological data. For example, suppose someone discovered a new protein in the lab but the sequence of the protein is not known. Using bioinformatics tools, the unknown protein is compared with a huge number of proteins stored in the database to predict a sequence based on similar patterns.
- Analysis– Pattern recognition is used for identifying important data trends. These trends can be used for future predictions. An analysis is required in almost every domain be it technical or non-technical. For example, the tweets made by a person on twitter helps in the sentiment analysis by identifying the patterns in the posts using natural language processing.
Advantages of Pattern Recognition
Using pattern recognition techniques provides a large number of benefits to an individual. It not only helps in the analysis of trends but also helps in making predictions.
- It helps in the identification of objects at varying distances and angles.
- Easy and highly automated.
- It is not rocket science and does not require an out of the box thinking ability.
- Highly useful in the finance industry to make valuable predictions regarding sales.
- Efficient solutions to real-time problems.
- Useful in the medical fields for forensic analysis and DNA (Deoxyribonucleic acid) sequencing.
Importance of Pattern Recognition in Machine Learning
- Pattern recognition identifies and predicts even the smallest of the hidden or untraceable data.
- It helps in the classification of unseen data.
- It makes suitable predictions using learning techniques.
- It recognizes and identifies an object at varying distances.
- It not only helps in the prediction of the unseen data but also helps in making useful suggestion.
Applications of Pattern Recognition
- Trend Analysis– Pattern recognition helps in identifying the trend in the given data on which appropriate analysis can be done. For example, looking at the recent trends in the sales made by a particular company or organization, future sales can be predicted.
- Assistance – Pattern is an integral part of our daily lives. It provides immense help in our day to day activities. A large number of software and applications are there in the market today that use machine learning algorithms to make predictions regarding the presence of obstacles and alerts the user to void miss happenings.
- E-Commerce – Visual search engines recognize the desired item based on its specifications and provide appropriate results. Most of the websites dedicated to online shopping make use of recommender systems. These systems collect data related to each customer purchase and make suggestions. All these tasks are accomplished by analyzing previous trends to make successful predictions.
- Computer vision– The user interacts with the system by giving an image or video as the input. The machine compares it with thousands or maybe millions of images stored in its database, to find similar patterns. The drawl of the essential features is done by using an algorithm that is mainly directed for grouping similar looking objects and patterns. This is termed as computer vision. Example, cancer detection.
- Biometric devices– These devices secure authentication and security by making using of face recognition and fingerprint detection technologies. On the hidden side, the base that enables the use of technologies like face and fingerprint recognition is machine learning algorithms.
Conclusion
Machine learning is one of the buzz words in the 21st century. It is highly in demand due to popular machine learning applications and advantages. It has revolutionized all the industries with its amazing capabilities. Machine learning has different fields and scopes some of which include pattern recognition, data mining, analysis, etc.
Pattern recognition in machine learning is widely used in almost every industry today be it technical or non-technical. It has helped in the analysis and visualization of various trends. It has not only increased the efficiency and ease of analysis and prediction making but has also increased the job opportunities in the field. Top-notch companies such as Microsoft, Google, Amazon are looking for individuals skilled in the art of pattern recognition and data analysis for making useful predictions. Thus, we can conclude by saying that pattern recognition is one of the most advancing fields in machine learning.
