Pattern Recognition: Fundamentals and Applications

By Fouad Sabry

What Is Pattern Recognition

The process of automatically recognizing patterns and regularities within data is known as pattern recognition. Statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics, and machine learning are just few of the fields that can benefit from its use. The fields of statistics and engineering are where pattern recognition got its start; some contemporary methods of pattern recognition involve the use of machine learning, which is made possible by the increased availability of huge data and the more abundant computing capacity. Both of these pursuits might be considered to be two facets of the same application sector, and both of these activities have undergone significant development over the course of the last several decades.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Pattern recognition

Chapter 2: Supervised learning

Chapter 3: Linear classifier

Chapter 4: Perceptron

Chapter 5: Gaussian process

Chapter 6: Expectation-maximization algorithm

Chapter 7: Generalized linear model

Chapter 8: Statistical learning theory

Chapter 9: Kernel method

Chapter 10: Probabilistic classification

(II) Answering the public top questions about pattern recognition.

(III) Real world examples for the usage of pattern recognition in many fields.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of pattern recognition.

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The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field.
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• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 5, 2023

Book preview

Chapter 1: Pattern recognition

The process of automatically recognizing patterns and regularities within data is known as pattern recognition. Statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics, and machine learning are just few of the fields that may benefit from its use. The fields of statistics and engineering are where pattern recognition got its start; some contemporary methods of pattern recognition involve the use of machine learning, which is made possible by the growing availability of huge data and the more abundant computing capacity. Both of these pursuits might be considered to be two sides of the same application sector, and both of these activities have seen significant growth over the course of the last several decades.

Training for pattern recognition systems often takes place using data that has been classified as training. When there are no labeled data available, alternative techniques may be utilized to identify patterns that were not known before. Both KDD and data mining place a greater emphasis on unsupervised analysis techniques and have a deeper relationship to practical applications in industry. The identification of patterns places a greater emphasis on the signal itself and also takes into account acquisition and signal processing. It has its roots in engineering, and nowadays the phrase is often used in the field of computer vision; in fact, one of the most prestigious conferences in the field of computer vision is called the Conference on Computer Vision and Pattern Recognition.

The process of applying a label to a particular input value is an example of pattern recognition in machine learning. In the field of statistics, discriminant analysis was first used in 1936 with the same goal in mind. Classification is an example of pattern recognition. In classification, an effort is made to allocate each input value to one of a predetermined group of classes (for example, determine whether a given email is spam or non-spam). The challenge of pattern recognition is a more broad one that involves recognizing patterns in a wider variety of outputs. Additional examples include regression, which associates a real-valued output with each real-valued input; In general, the goal of pattern recognition algorithms is to offer a fair response for all of the potential inputs and to conduct most probable matching of the inputs while taking into consideration the statistical variance that may exist in the inputs. Pattern matching algorithms, on the other hand, seek perfect matches in the input with pre-existing patterns. This stands in contrast to those algorithms. Regular expression matching is a common example of a pattern-matching algorithm. This algorithm searches for patterns of a certain kind in textual data and is included in the search capabilities of many text editors and word processors. Other examples of pattern-matching algorithms include fuzzy pattern matching and string matching.

One definition of pattern recognition in the current era is:

The study of patterns is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories. One of the primary goals of the field of pattern recognition is to automate the process of finding regularities in data.

In general, different types of pattern recognition are classified according to the kind of learning technique that was used to produce the output value. In supervised learning, it is assumed that a set of training data, also known as the training set, has been supplied. This data set should include a collection of instances that have been correctly labeled by hand with the appropriate output. Then, a learning mechanism will develop a model that will seek to achieve two goals that are often in competition with one another: Perform as well as possible on the training data and generalize as well as possible to new data (often, this implies being as simple as feasible, using some technical meaning of simple in line with Occam's Razor, which will be explored further below). Unsupervised learning, on the other hand, makes the assumption that the training data has not been hand-labeled. It then makes an effort to discover inherent patterns in the data, which can then be used to determine the appropriate output value for new data instances. This type of learning is called deep learning. Semi-supervised learning, which makes use of both labeled and unlabeled data, is an approach that combines the two and has been investigated as a possible mixture of the two (typically a small set of labeled data combined with a large amount of unlabeled data). In situations involving unsupervised learning, there is a possibility that there will be no training data at all.

When referring to the respective supervised and unsupervised learning techniques for the same kind of output, it is not uncommon to hear various terminology being used to describe the processes. The common perception of the task is that it requires no training data to speak of, and of grouping the input data into clusters based on some inherent similarity measure (for example, the distance between instances, considered as vectors in a multi-dimensional vector space), rather than assigning each input instance into one of a set of pre-defined classes. As a result, the unsupervised equivalent of classification is normally referred to as clustering. This is because of the common perception that the task involves no training data to speak of The language used in some disciplines is distinct from that used in others. In the field of community ecology, the word clustering is equivalent to what is referred to by the more popular term classification..

In technical parlance, the piece of input data that results in the generation of a value for output is referred to as an instance. Formally, the instance is characterized by a vector of features, which, when taken as a whole, give a description of all of the instance's known qualities. These feature vectors can be interpreted as defining points in an appropriate multidimensional space. Methods for manipulating vectors in vector spaces, such as computing the dot product or the angle between two vectors, can correspondingly be applied to these feature vectors in order to manipulate them in the desired manner. In general, features are either categorical (also known as nominal, i.e., consisting of one of a set of unordered items, such as a gender of male or female, or a blood type of A, B, AB, or O), ordinal (consisting of one of a set of ordered items, e.g., large, medium, or small), integer-valued (e.g. (e.g., a measurement of blood pressure). It is common practice to combine data of a categorical and ordinal nature, as is the practice of combining data of an integer-valued and a real-valued variety. A