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Pattern Recognition Systems

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[The Seven Patterns of AI - Javatpoint]


- Overview

What makes pattern recognition unique is that efforts to solve image recognition problems have led to increased interest in deep learning techniques for AI, and have also helped to unlock new trends in AI investment and enthusiasm. 

However, recognizing patterns is much broader than image recognition. In fact, we can use machine learning to detect and understand objects such as sounds, images, handwriting, faces, gestures, and facial expressions. The purpose of this design is to make machines that can recognize and understand unstructured data. Due to its numerous applications, this type of AI is a major part of artificial intelligence solutions. 

Pattern recognition is a data analysis method that uses machine learning algorithms to automatically identify patterns and patterns in data. This data can be anything from text and images to sounds or other definable qualities. Pattern recognition systems can quickly and accurately identify familiar patterns.

Pattern recognition has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics, and machine learning. 


- Understanding AI's Recognition Patterns

Of the seven AIs that illustrate different ways of implementing AI, the most popular one is recognizing patterns. The principles behind identifying patterns in AI involves the use of machine learning and cognitive techniques to categorize and classify unstructured data into distinct classifications.

Unstructured data may include images, videos, text, and even quantitative data. What makes it so effective is that it allows machines to easily do what our brains do: detect what we see in the real world around us.


- Pattern Recognition and Machine Learning

In the human brain (which artificial intelligence and machine learning try to mimic), pattern recognition is the cognitive process that occurs in the brain when it matches information we see with data stored in our memory. 

However, when we talk about computer science, pattern recognition is a technique of matching incoming data with information stored in a database. Hence, pattern recognition is a type of machine learning as it uses machine learning algorithms to identify patterns.

Pattern recognition has its roots in statistics and engineering; due to the increased availability of big data and the abundance of new processing power, some modern methods of pattern recognition include the use of machine learning. These activities can be viewed as two sides of the same application area and have undergone substantial development over the past few decades. 

We often see that when people give a definition of machine learning or deep learning, they use words like "a process that mimics the human brain." Now, what do they mean when they say "mimic the human brain"? If the system has enough computing power and enough data to process it, it can solve the most challenging problems. 


- Features of Pattern Recognition

Pattern recognition and machine learning detect arrangements of data features that reveal information about a given dataset or system and have the following four characteristics: 

  • it learns from data
  • It automatically recognizes the pattern even if it is partially visible
  • It can recognize familiar patterns
  • Recognize from different shapes and angles

In other words, pattern recognition and machine learning are two sides of the same coin. 

For example, stock fluctuations and signs of cancer in mammograms, computers with trained programs can identify these better than humans. Now we have to understand what are the complex processes behind these algorithms and how these algorithms mimic the human brain and provide better results.


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- Steps To Prepare Data for Patterns

Before searching for patterns, there are specific steps, the first of which is to collect data from the real world. The collected data needs to be filtered and preprocessed so that its system can extract features from the data. Then select the appropriate algorithm in classification, Clustering, and regression to identify patterns according to the type of data system.

  • Classification. In classification, the algorithm assigns labels to data based on predefined characteristics. This is an example of supervised learning.
  • Clustering. An algorithm divides data into many clusters based on similarity of features. This is an example of unsupervised learning.
  • Regression. Regression algorithms try to find relationships between variables and predict unknown dependent variables based on known data. It is based on supervised learning.


Features can be represented as continuous, discrete or discrete binary variables. A feature is basically a function of one or more measurements, computed to quantify important characteristics of an object. This function is one of the most important components in a pattern recognition system.

Example: Consider a football. Shape, size, and color are all characteristics of footballs.


- Pattern Recognition Systems

Pattern recognition systems are typically trained from labeled "training" data. When no labeled data is available, other algorithms can be used to discover previously unknown patterns. KDD and data mining are more focused on unsupervised methods and stronger connections to business usage. Pattern recognition focuses more on the signal and also considers acquisition and signal processing. It originated in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named the Conference on Computer Vision and Pattern Recognition. 

In machine learning, pattern recognition is the assignment of labels to given input values. In statistics, discriminant analysis was introduced in 1936 for the same purpose. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of categories (eg, to determine whether a given email is "spam" or "not spam"). 

Pattern recognition is a more general problem, and it encompasses other types of output as well. Other examples are regression, which assigns a real-valued output to each input; sequence tokens, which assign a class to each member of a sequence of values ​​(e.g., part-of-speech tokens, which assign a part of the speech of each word in the input sentence) ; and parsing, which assigns a parse tree to the input sentence, describing the syntactic structure of the sentence. 


- Pattern Recognition Algorithms

Pattern recognition algorithms generally aim to provide reasonable answers for all possible inputs, and perform a "most likely" match of the inputs taking into account their statistical variation. This is in contrast to pattern-matching algorithms, which look for matches in the input that exactly match pre-existing patterns. 

A common example of a pattern matching algorithm is regular expression matching, which finds patterns of a given type in text data and is included in the search capabilities of many text editors and word processors. 



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