Pattern Recognition Systems

- Overview

Pattern recognition (PR) is the process of identifying patterns using machine learning (ML) algorithms. PR can be defined as the classification of data based on acquired knowledge or on statistics extracted from patterns and/or their representations. 

One of the important aspects of PR is its application potential. For example, speech recognition, speaker recognition, multimedia document recognition (MDR), automated medical diagnosis.

In a typical PR application, raw data is processed and converted into a form suitable for machine use. PR involves the classification and clustering of patterns. 

In classification, appropriate category labels are assigned to patterns based on abstractions generated using a set of training patterns or domain knowledge. Classification is used for supervised learning.

Clustering produces partitions of data that facilitate decision-making, i.e., the specific decision-making activities of which we are interested. Clustering is used for unsupervised learning.

- Classifications of Pattern Recognition

Pattern recognition (PR) involves two primary classification methods:

• Supervised classification: In a supervised PR method, a human trains a computer algorithm to recognize patterns based on predefined labeled datasets. Once the pattern is identified, the method subsequently classifies new data.
• Unsupervised classification: In unsupervised classification, the model learns independently without any direct guidance from a human. The computer algorithm identifies correlations between multiple data elements (inputs) based on their similarity score and performs data classification.

- Understanding AI's Recognition Patterns

Of the seven AIs (above) 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.

AI's recognition patterns use machine learning (ML) and cognitive technology to identify and categorize unstructured data into specific classifications. This data can be images, video, text, or even quantitative data. 

Pattern recognition in AI is the process of identifying regularities or patterns in data. This process allows machines to discern and interpret meaningful information. 

Here are some steps of pattern recognition: 

• Identify common elements in problems or systems
• Identify and interpret common differences in problems or systems
• Identify individual elements within problems
• Describe patterns that have been identified
• Make predictions based on identified patterns

Some common methods for pattern recognition in AI include: 

• Structural pattern recognition: Focuses on analyzing the structural relationships between data elements
• Statistical pattern recognition: Uses mathematical models and algorithms to identify patterns from large datasets
• Feature extraction: Involves selecting and representing the most relevant information or attributes from the raw data

- 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.

- 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.