Other Uses of Pattern Recognition
One of the most promising areas for using pattern recognition is that of biometric identification programs. Making identification fast and accurate, it elevates the levels of security in different areas and improves people’s experience with various products and services.
That’s why at RecFaces, we focus on developing software products that help businesses to increase their performance with the benefits of biometric identification. Our software levels up the functionality of CCTV, helping to regulate access to control systems, supervise the personnel, or use the gym comfortably.
With pattern recognition technologies, we assist in improving the security and quality of management and user experience in educational and medical institutions, industrial facilities, banks, hotels, gyms, and other institutions.
What is an example of pattern recognition?
As an example of natural pattern recognition, one can think about filling in the missing letters in a word. For technology, it could be the camera detecting faces when the photo is being taken or the phone memo app turning voice into text.
What are the types of pattern recognition?
There are three main types of pattern recognition, dependent on the mechanism used for classifying the input data. Those types are: statistical, structural (or syntactic), and neural. Based on the type of processed data, it can be divided into image, sound, voice, and speech pattern recognition.
How do humans recognize patterns?
The human brain is evolutionarily wired to recognize patterns in the surrounding environment. To do so, it constantly matches the current sensory input with the information from the previous experience stored in the long-term memory.
Is pattern recognition a sign of intelligence?
Being a foundation for predicting and making decisions, pattern recognition is one of the main factors that determine the level of general intelligence. The ability to recognize patterns can be regarded as a sign of intelligence, although the exact definition of the latter may vary.
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Types of Pattern Recognition
This type of pattern recognition enables identifying particular objects depicted in images. Image recognition is a core part of computer vision, which is basically the ability of a machine to recognize images and take corresponding actions (e.g., a self-driving car slowing down after identifying a pedestrian ahead).
Image recognition is commonly implemented in such processes as:
- Visual search;
- OCR (optical character recognition);
- Face detection.
This type of pattern recognition is used for identifying various sounds. By analyzing audio signals, the system labels them as belonging to a certain category. Here are some examples where sound pattern recognition can be used:
- Surveillance alarm detection;
- Identifying animal species;
- Melody recognition.
This type of pattern recognition analyzes the sounds of a human voice to identify the speaker. Unlike speech recognition, it does not involve language processing and solely spots personal characteristics in a speaking pattern. It is used mostly for security purposes (personal identification). Common areas of usage include:
- Mobile or web applications;
- Internet of things.
Much like optical character recognition identifies letters and words on the image, speech recognition captures elements of a language in the sound of a person speaking. For this technology, widespread areas of usage include:
- Voice-to-text converters;
- Auto captioning for videos;
- Virtual assistants.
The Basic Components of Pattern Recognition Systems
Every machine learning-based pattern recognition algorithm includes the following steps.
- Input of data. Large amounts of data enter the system through different sensors.
- Preprocessing or segmentation. At this stage, the system groups the input data to prepare the sets for future analysis.
- Feature selection (extraction). The system searches for and determines the distinguishing traits of the prepared sets of data.
- Classification. Based on the features detected in the previous step, data is assigned a class (or cluster), or predicted values are calculated (in the case of regression algorithms).
- Postprocessing. According to the outcome of the recognition, the system performs future actions.
Neural Networks for Pattern Recognition
Using neural networks for pattern recognition is the most flexible approach to the task. These networks are comparatively autonomous in learning to recognize patterns and are capable of constant development and self-organization. Neural networks make it possible to solve tasks that could probably never be solved using only statistical algorithms.
This adaptiveness and functionality make neural networks widely implemented in pattern recognition software. The most popular type in this area is feedforward neural networks, where information moves in one direction only. Those are often used for tasks of speech recognition or identifying objects.
Opening your repertoire
Learning your repertoire begins by logging in with your ChessBase account.
Now choose «Easy Game» and continue with the «Openings» tab:
Click the tab «My Moves» and the following appears on your screen:
Now load your opening repertoire, either for White («Load White») or for Black («Load Black»). The repertoire will appear in the notation:
Learning your Repertoire
Fritz 17 comes with a new function to learn and remember your repertoire: replaying the repertoire which allows you to relax while your program replays your repertoire on the screen.
Click «Replay» (on the very right of the toolbar):
This opens the following menu:
The bar allows you to decide whether the repertoire is shown from the end or from the beginning. The blue button starts and stops the replay.
«Speed» allows you to regulate the tempo of the replay:
«N Repeats» allows you to determine how often each variation is shown:
This allows you to choose your «program», so to speak. «Lock on variation» tells the program to show another variation when the end of the given variation is reached:
You can see how much of your repertoire you’ve reviewed
Memorizing your Repertoire
The «Drill» is a new function in Fritz 17 that helps you to memorize your opening repertoire. It also allows you to check how well you remember your repertoire — the program will provide hard facts and numbers.
«Load White/Load Black» activates the repertoire. To start the drill click on «Drill White» or «Drill Black».
The following menu appears:
You can enter the position with which you want to start the drill on the board (e.g. 1.e4 c5) or you can begin from the starting position. When you are ready, click «Start Drill».
In this example, the repertoire consists of 355 moves:
In a previous drill session a «Memory Score» of 6.2 points was reached (about 1% of all repertoire moves) and 14% of the positions were covered. So, there’s still room for improvement!
Now start the drill and enter the moves which you think are part of your repertoire on the board.
When you reach the end of the line the program tells you that the «Drill (is) successfully done» and «You can now play a training game» against «Fritz-Online».
If you want to have another try, click «Repeat Drill».
If you forgot your repertoire move the program gives feedback and a hint to help you remember the right repertoire move. Just try again!
The bulbs indicate how much you have learned — the amount of moves you remember
The «Drill-Ranking» is another playful competition to stimulate you to learn your repertoire. To see how well you remember your repertoire compared to others click
If you are online, you are connected to the ChessBase Opening App and a ranking list appears. The ranking list counts the points you gained at the «Drill» and shows you how you fare compared to others.
If you do more drills, you remember your repertoire better, then you gain more points and will climb the ranking list. However, you cannot rest on your laurels. To pay tribute to the fact that humans forget, points will be deducted from your score over time. So keep practicing!
If you did not enter an opening repertoire with Fritz 17 or if you just want to check quickly — e.g. to prepare for a game — how well you remember a certain line, the «Free Drill» is ideal.
Load the line that you want to drill and go to the position with which you want to start. Then click:
And start the drill with the following menu:
You can start the drill at a position of your choice. Then follow your progress:
The next tutorials will show how Fritz 17 helps to create an opening repertoire.
Pattern Recognition Examples
Examples of pattern recognition can be easily found in nature, like humans recognizing faces or pets responding to their names. In technology, pattern recognition algorithms trained through machine learning are applied in various fields, ranging from everyday tasks to highly specialized areas.
Here are some typical examples:
- NLP (natural language processing): virtual assistants, speech-to-text interfaces, automatic captioning;
- OCR scanners (optical character recognition): mobile scanner apps;
- Medical diagnostic software;
- Meteorological forecast software;
- NIDS (Network intrusion detection systems): security systems, which recognize patterns of suspicious activities.
Why Is Pattern Recognition Important?
Nowadays, pattern recognition serves as a basis for a number of technologies used in everyday life. Face recognition can be one of the most common examples of implementing pattern recognition on a complex level, as it involves processing a large set of visual elements that make a person’s face unique.
Face recognition, as well as other biometrics technologies, have already tremendously influenced the process of identity verification and will continue to influence our society.
Besides, pattern recognition is an irreplaceable analytical tool as well. Complex big data analyses, like stock market prediction, business analytics, or medical diagnostics rely on pattern-recognizing algorithms. WIthout seamless pattern recognition, drawing meaningful conclusions from large sets of data would be impossible.
Pattern Recognition Algorithm for Machine Learning
Pattern recognition algorithms are inseparable from machine learning. When training the pattern recognizer, supervised and unsupervised learning approaches are commonly utilized.
In supervised machine learning, the human participant prepares representative sets of data (referred to as training sets) designed to illustrate the patterns which the system is expected to learn to recognize. After processing those sets, the system’s performance is checked by exposing it to other data of a similar format, organized in so-called test sets.
The kind of pattern recognition trained this way is referred to as classification.
When machine learning is unsupervised, the involvement of a human component and pre-existing patterns is reduced to a minimum. In this case, the algorithm is trained to detect new patterns without using any already existing labels, just by being introduced to the large sets of data. Hierarchical or k-means clustering algorithms are often used in this approach. Consequently, the pattern recognition obtained through this type of learning is referred to as clustering.
Alongside machine learning, deep learning is also implemented in training pattern recognizers when it comes to neural networks.
Pattern Recognition Approaches
There are three basic approaches that pattern recognition algorithms utilize:
- Statistical. This approach is based on statistical decision theory. Pattern recognizer extracts quantitative features from the data along with the multiple samples and compares those features. However, it does not touch upon how those features are related to each other.
- Structural (a.k.a. syntactic). This approach is closer to how human perception works. It extracts morphological features from one data sample and checks how those are connected and related.
- Neural. In this approach, artificial neural networks are utilized. Compared to the ones mentioned above, it allows more flexibility in learning and is the closest to natural intelligence.
Pattern Recognition and Machine Learning (ML)
Patterns are everywhere. It belongs to every aspect of our daily lives. Starting from the design and colour 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 been doing probably since childhood. When we were in school, we were often given the task of identifying the missing alphabets or 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 exactly means.
What is meant by 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
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-mans clustering.
- Based on the features or properties of the new object, it is assigned to a group to make a prediction.