Pattern Recognition and Machine Learning in Simple Words

One of the most common applications of machine learning is pattern recognition. Computers that use well-trained algorithms recognize animals in photos, anomalies in stock fluctuations, and signs of cancer in mammograms much better than humans. Let us find out what lies behind this complex process.

What is pattern recognition?

Pattern recognition is the process of recognizing regularities in data by a machine that uses machine learning algorithms. In the heart of the process lies the classification of events based on statistical information, historical data, or the machine’s memory.

A pattern is a regularity in the world or in abstract notions. If we talk about books or movies, a description of a genre would be a pattern. If a person keeps watching black comedies, Netflix wouldn’t recommend them heartbreaking melodramas.

The most popular programming language for pattern recognition is Python. Check out our Python consulting services to learn more about solutions that will help you create forecasts and automate your processes.

For the machine to search for patterns in data, it should be preprocessed and converted into a form that a computer can understand. Then, the researcher can use classification, regression, or clustering algorithms depending on the information available about the problem to get valuable results:

  • Classification. In classification, the algorithm assigns labels to data based on the predefined features. This is an example of supervised learning. More about classification algorithms you can read in our blog.
  • Clustering. An algorithm splits data into a number of clusters based on the similarity of features. This is an example of unsupervised learning.
  • Regression. Regression algorithms try to find a relationship between variables and predict unknown dependent variables based on known data. It is based on supervised learning.

What should a pattern recognition system be able to do?

If you want to assess how good or bad a pattern recognition system is, you need to pay attention to what it can do:

  • Identify a familiar pattern quickly and accurately.
  • Classify unfamiliar objects.
  • Recognize shapes and objects from different angles.
  • Uncover patterns and objects, even when partly hidden.
  • Automatically recognize patterns.

Training a pattern recognition system

To build a pattern recognition system, you need to choose a model and prepare the data. For pattern recognition, neural networks, classification algorithms (Naive Bayes, Decision Tree, Support Vector Machines), or clustering algorithms (k-means, Mean Shift, DBSCAN) are often used.

Next, you will work with data. Divide it into three sets:

Training, validation, and test sets
  • Training set. We use the training set to train the model. You need to select representative samples and make the program process it using training rules. For example, if you are building a security system based on face recognition, you will need a variety of photos of your employees. All the relevant information will be extracted from these data. Generally, 80% of all the data makes a training dataset.
  • Validation set. This set is used to fine-tune the model. You use it to verify that any increase in accuracy over the training data set will also show an increase in accuracy over a data set that has not been shown to the network before. Because if the accuracy over the training data set increases, but the accuracy over the validation data set stays the same or decreases, then you’re overfitting your model and you should stop training it.
  • Testing set. Testing data is used to test whether the outputs given by the system are accurate. About 20% of the data is used for testing.

Note: do not confuse the validation set and testing set. The validation set is used to tune the parameters of the model while a testing set assesses its performance as a whole.

Components of a pattern recognition system

Pattern recognition system

A pattern recognition system needs some input from the real world that it perceives with sensors. Such a system can work with any type of data: images, videos, numbers, or texts.

Having received some information as the input, the algorithm performs preprocessing. That is segmenting something interesting from the background. For example, when you are given a group photo and a familiar face attracts your attention, this is preprocessing.

Preprocessing is tightly connected with enhancement. By this term, researchers understand an increase in the ability of a human or a system to recognize patterns even when they are vague. Imagine you are still looking at the same group photo but it is 20 years old. To make sure that the familiar face in the photo is really the person you know, you start comparing their hair, eyes, and mouth. This is when enhancement steps into the game.

The next component is feature extraction. The algorithm uncovers some characteristic traits that are similar to more than one data sample.

The result of a pattern recognition system will be either a class assignment (if we used classification), or cluster assignment (in case of clustering), or predicted values (if you apply regression).

How does pattern recognition work?

There are three types of pattern recognition models:

Pattern recognition models

Statistical Pattern Recognition

This type of pattern recognition refers to statistical historical data when it learns from examples: it collects observations, processes them, and learns to generalize and apply these rules to new observations.

Syntactic Pattern Recognition

It is also called structural pattern recognition because it relies on simpler subpatterns called primitives (for example, words). The pattern is described in terms of connections between the primitives, for example, words form sentences and texts.

Neural Pattern Recognition

In neural pattern recognition, artificial neural networks are used. They can learn complex nonlinear input-output relations and adapt themselves to the data.

Pattern recognition process

Pattern recognition happens in two stages.

  • First goes the explorative part. The algorithm is searching for patterns in general.
  • Next, there is the descriptive part, where the algorithm begins to categorize the found patterns.

The combination of the two is used to extract insights.

The process itself looks like this:

Pattern recognition process
  • First, you need to gather data.
  • Then, you preprocess it and clean it from the noise.
  • The algorithm examines the data and looks for relevant features or common elements.
  • Then, these elements are classified or clustered;
  • Each segment is analyzed for insights;
  • Finally, the extracted insights are implemented in practice.

Where to use pattern recognition

Pattern recognition techniques are useful for solving classification problems, uncovering fraud, predicting volcanic eruptions, or diagnosing dangerous diseases with higher accuracy than humans. What is an example of pattern recognition?

Image processing, segmentation, and analysis

Pattern recognition is used for image processing. For example, a machine learning algorithm can recognize hundreds of bird species better than humans, even if the image is dark or noisy.

Computer vision

One of the artificial neural networks that use pattern recognition for computer vision is Neural Talk that can generate descriptions of the environment in real-time.

Speech recognition

Virtual assistants such as Alexa or Siri use speech recognition systems to process whole chunks of speech instead of working with separate words of phonemes.

Fingerprint identification

A number of recognition methods have been used to perform fingerprint matching. The pattern recognition approach is widely used both for criminalistics and in your own smartphone. If you have a fingerprint lock on your phone, that is pattern recognition that steps into the game every time you unlock it.

Stock market analysis

The stock market is hard to predict. However, even there, there are patterns that can be recognized and used. Modern apps for investors use AI to provide their users with consulting services. Some examples are Blumberg, Tinkoff, Kosho, and SofiWealth.

Medical diagnosis

Algorithms for pattern recognition trained on real data can be used for cancer diagnosis. These researchers have proposed an automatic breast cancer detection technique that gives a prediction accuracy of 99.86%. They used histopathology images from biopsy for feature extraction and applied an artificial neural network to produce the results.

Final thoughts

Pattern recognition algorithms analyze data and make accurate predictions that help businesses and people who own businesses make the right decisions. If need be, pattern recognition can fully automate solving complex analytical tasks.

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