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Data Complexity in Pattern Recognition

Data Complexity in Pattern Recognition
Author: Mitra Basu
Publisher: Springer Science & Business Media
Total Pages: 309
Release: 2006-12-22
Genre: Computers
ISBN: 1846281725
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Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its role in shaping theory and techniques across many disciplines, probing strengths and deficiencies of current classification techniques, and the algorithms that drive them. The book offers guidance on choosing pattern recognition classification techniques, and helps the reader set expectations for classification performance.

Advanced Pattern Recognition Techniques

Advanced Pattern Recognition Techniques
Author: North Atlantic Treaty Organization. Research and Technology Organization
Publisher:
Total Pages: 172
Release: 1998
Genre: Artificial intelligence
ISBN:
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Pattern recognition is the extraction of consistent information from noisy spatiotemporal data. It can be and is currently being used in systems for battlefield supervision, smart weapons, and anti-counterfeiting of all kinds. A currrent application is the automatic detection of land mines and unexploded ordnance. (UXO). The methods employed can be subdivided in the following manner: (1) statistical methods, (2) neuro - methods, (3) fuzzy - methods, and (4) neuro fuzzy methods. Each of these methods has its special advantages and drawbacks, but all of them require the computation of feature variables from measurement or simulation data, e.g. from microwave backscattering. The Lecture series covers the following topics: (1) Introductory overview on pattern recognition techniques, (1) - (4); (2)Feature extraction for pattern recognition by; (a)Electromagnetic, magnetic, and acoustic singularity identification; (b)Model based scattering signatures; (c) Wavelet techniques; (d) SAR/ISAR imaging; (e)Bistatic microwave imaging; and (f)Electromagnetic inversion techniques; (3) Real-time implementation of pattern recognition methods; and (4)Introduction to software and hardware for pattern recognition.

Image Processing and Pattern Recognition

Image Processing and Pattern Recognition
Author: Frank Y. Shih
Publisher: John Wiley & Sons
Total Pages: 564
Release: 2010-05-03
Genre: Technology & Engineering
ISBN: 0470404612
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A comprehensive guide to the essential principles of image processing and pattern recognition Techniques and applications in the areas of image processing and pattern recognition are growing at an unprecedented rate. Containing the latest state-of-the-art developments in the field, Image Processing and Pattern Recognition presents clear explanations of the fundamentals as well as the most recent applications. It explains the essential principles so readers will not only be able to easily implement the algorithms and techniques, but also lead themselves to discover new problems and applications. Unlike other books on the subject, this volume presents numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. Scores of graphs and examples, technical assistance, and practical tools illustrate the basic principles and help simplify the problems, allowing students as well as professionals to easily grasp even complicated theories. It also features unique coverage of the most interesting developments and updated techniques, such as image watermarking, digital steganography, document processing and classification, solar image processing and event classification, 3-D Euclidean distance transformation, shortest path planning, soft morphology, recursive morphology, regulated morphology, and sweep morphology. Additional topics include enhancement and segmentation techniques, active learning, feature extraction, neural networks, and fuzzy logic. Featuring supplemental materials for instructors and students, Image Processing and Pattern Recognition is designed for undergraduate seniors and graduate students, engineering and scientific researchers, and professionals who work in signal processing, image processing, pattern recognition, information security, document processing, multimedia systems, and solar physics.

Advanced Pattern Recognition Technologies with Applications to Biometrics

Advanced Pattern Recognition Technologies with Applications to Biometrics
Author: Zhang, David
Publisher: IGI Global
Total Pages: 384
Release: 2009-01-31
Genre: Education
ISBN: 1605662011
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"This book focuses on two kinds of advanced biometric recognition technologies, biometric data discrimination and multi-biometrics"--Provided by publisher.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
Author: Christopher M. Bishop
Publisher: Springer
Total Pages: 0
Release: 2016-08-23
Genre: Computers
ISBN: 9781493938438
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This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Pattern Recognition Techniques, Technology and Applications

Pattern Recognition Techniques, Technology and Applications
Author: Danel Jaso
Publisher:
Total Pages: 298
Release: 2016-04-01
Genre:
ISBN: 9781681174648
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"Pattern Recognition Techniques, Technology and Applications disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition. The book provides comprehensive overview of the developments of techniques and approaches on pattern recognition. Pattern recognition is the science of making inferences from perceptual data, using tools from statistics, probability, computational geometry, machine learning, signal processing, and algorithm design. A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition task. Pattern recognition is the imposition of identity on input data, such as speech, images, or a stream of text, by the recognition and delineation of patterns it contains and their relationships. Stages in pattern recognition may involve measurement of the object to identify distinguishing attributes, extraction of features for the defining attributes, and comparison with known patterns to determine a match or mismatch. Pattern recognition has extensive application in astronomy, medicine, robotics, and remote sensing by satellites."

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Author: Christopher M. Bishop
Publisher: Oxford University Press
Total Pages: 501
Release: 1995-11-23
Genre: Computers
ISBN: 0198538642
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Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.

Pattern Recognition Techniques Applied to Biomedical Problems

Pattern Recognition Techniques Applied to Biomedical Problems
Author: Martha Refugio Ortiz-Posadas
Publisher: Springer Nature
Total Pages: 227
Release: 2020-02-29
Genre: Mathematics
ISBN: 3030380211
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This book covers pattern recognition techniques applied to various areas of biomedicine, including disease diagnosis and prognosis, and several problems of classification, with a special focus on—but not limited to—pattern recognition modeling of biomedical signals and images. Multidisciplinary by definition, the book’s topic blends computing, mathematics and other technical sciences towards the development of computational tools and methodologies that can be applied to pattern recognition processes. In this work, the efficacy of such methods and techniques for processing medical information is analyzed and compared, and auxiliary criteria for determining the correct diagnosis and treatment strategies are recommended and applied. Researchers in applied mathematics, the computer sciences, engineering and related fields with a focus on medical applications will benefit from this book, as well as professionals with a special interest in state-of-the-art pattern recognition techniques as applied to biomedicine.

Machine Learning and Big Data

Machine Learning and Big Data
Author: Uma N. Dulhare
Publisher: John Wiley & Sons
Total Pages: 544
Release: 2020-09-01
Genre: Computers
ISBN: 1119654742
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This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.

Handbook of Pattern Recognition and Computer Vision

Handbook of Pattern Recognition and Computer Vision
Author: C. H. Chen
Publisher: World Scientific
Total Pages: 1045
Release: 1999
Genre: Computers
ISBN: 9812384731
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The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference.