Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
Available:
Author: Christopher M. Bishop
Pages: 738
ISBN: 0387310738
Release: 2006-08-17
Editor: Springer Verlag

DESCRIPTION OF THE BOOK:

This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical models to describe probability distributions when there are no other books that apply graphical models to machine learning. It is also the first four-color book on pattern recognition. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
Available:
Author: Christopher M. Bishop
Pages: 738
ISBN: 1493938436
Release: 2016-08-23
Editor: Springer

DESCRIPTION OF THE BOOK:

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 and Machine Learning

Pattern Recognition and Machine Learning
Available:
Author: Christopher M. Bishop
Pages: 738
ISBN: 8132209060
Release: 2013
Editor: Unknown

DESCRIPTION OF THE BOOK:

The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
Available:
Author: King-Sun Fu
Pages: 344
ISBN: 9781461575665
Release: 2012-12-06
Editor: Springer Science & Business Media

DESCRIPTION OF THE BOOK:

This book contains the Proceedings of the US-Japan Seminar on Learning Process in Control Systems. The seminar, held in Nagoya, Japan, from August 18 to 20, 1970, was sponsored by the US-Japan Cooperative Science Program, jointly supported by the National Science Foundation and the Japan Society for the Promotion of Science. The full texts of all the presented papers except two t are included. The papers cover a great variety of topics related to learning processes and systems, ranging from pattern recognition to systems identification, from learning control to biological modelling. In order to reflect the actual content of the book, the present title was selected. All the twenty-eight papers are roughly divided into two parts--Pattern Recognition and System Identification and Learning Process and Learning Control. It is sometimes quite obvious that some papers can be classified into either part. The choice in these cases was strictly the editor's in order to keep a certain balance between the two parts. During the past decade there has been a considerable growth of interest in problems of pattern recognition and machine learn ing. In designing an optimal pattern recognition or control system, if all the a priori information about the process under study is known and can be described deterministically, the optimal system is usually designed by deterministic optimization techniques.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
Available:
Author: Y. Anzai
Pages: 407
ISBN: 9780080513638
Release: 2012-12-02
Editor: Elsevier

DESCRIPTION OF THE BOOK:

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

Pattern Recognition and Machine Intelligence

Pattern Recognition and Machine Intelligence
Available:
Author: B. Uma Shankar,Kuntal Ghosh,Deba Prasad Mandal,Shubhra Sankar Ray,David Zhang,Sankar K. Pal
Pages: 695
ISBN: 9783319699004
Release: 2017-12-06
Editor: Springer

DESCRIPTION OF THE BOOK:

This book constitutes the proceedings of the 7th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2017,held in Kolkata, India, in December 2017. The total of 86 full papers presented in this volume were carefully reviewed and selected from 293 submissions. They were organized in topical sections named: pattern recognition and machine learning; signal and image processing; computer vision and video processing; soft and natural computing; speech and natural language processing; bioinformatics and computational biology; data mining and big data analytics; deep learning; spatial data science and engineering; and applications of pattern recognition and machine intelligence.

Fundamentals of Pattern Recognition and Machine Learning

Fundamentals of Pattern Recognition and Machine Learning
Available:
Author: Ulisses de Mendonça Braga-Neto
Pages: 329
ISBN: 9783030276560
Release: 2020
Editor: Unknown

DESCRIPTION OF THE BOOK:

Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification. The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website.

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Available:
Author: Christopher M. Bishop
Pages: 482
ISBN: 9780198538646
Release: 1995-11-23
Editor: Oxford University Press

DESCRIPTION OF THE BOOK:

`Readers will emerge with a rigorous statistical grounding in the theory of how to construct and train neural networks in pattern recognition' New Scientist

Sequential Methods in Pattern Recognition and Machine Learning

Sequential Methods in Pattern Recognition and Machine Learning
Available:
Author: K.C. Fu
Pages: 226
ISBN: 9780080955599
Release: 1968
Editor: Academic Press

DESCRIPTION OF THE BOOK:

Sequential Methods in Pattern Recognition and Machine Learning

Fundamentals of Pattern Recognition and Machine Learning

Fundamentals of Pattern Recognition and Machine Learning
Available:
Author: Ulisses Braga-Neto
Pages: 357
ISBN: 3030276554
Release: 2020-11-01
Editor: Springer

DESCRIPTION OF THE BOOK:

Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University. The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification. The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website.

Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security

Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security
Available:
Author: Dua, Mohit,Jain, Ankit Kumar
Pages: 355
ISBN: 9781799833017
Release: 2021-05-14
Editor: IGI Global

DESCRIPTION OF THE BOOK:

The artificial intelligence subset machine learning has become a popular technique in professional fields as many are finding new ways to apply this trending technology into their everyday practices. Two fields that have majorly benefited from this are pattern recognition and information security. The ability of these intelligent algorithms to learn complex patterns from data and attain new performance techniques has created a wide variety of uses and applications within the data security industry. There is a need for research on the specific uses machine learning methods have within these fields, along with future perspectives. The Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security is a collection of innovative research on the current impact of machine learning methods within data security as well as its various applications and newfound challenges. While highlighting topics including anomaly detection systems, biometrics, and intrusion management, this book is ideally designed for industrial experts, researchers, IT professionals, network developers, policymakers, computer scientists, educators, and students seeking current research on implementing machine learning tactics to enhance the performance of information security.

Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Available:
Author: Brian D. Ripley
Pages: 403
ISBN: 0521717701
Release: 2007
Editor: Cambridge University Press

DESCRIPTION OF THE BOOK:

This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.

Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition

Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition
Available:
Author: Serkan Kiranyaz,Turker Ince,Moncef Gabbouj
Pages: 321
ISBN: 9783642378461
Release: 2013-07-16
Editor: Springer Science & Business Media

DESCRIPTION OF THE BOOK:

For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach. After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterized by strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets. The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
Available:
Author: King-Sun Fu
Pages: 343
ISBN: WISC:89037590429
Release: 1971-07
Editor: Springer

DESCRIPTION OF THE BOOK:

This book contains the Proceedings of the US-Japan Seminar on Learning Process in Control Systems. The seminar, held in Nagoya, Japan, from August 18 to 20, 1970, was sponsored by the US-Japan Cooperative Science Program, jointly supported by the National Science Foundation and the Japan Society for the Promotion of Science. The full texts of all the presented papers except two t are included. The papers cover a great variety of topics related to learning processes and systems, ranging from pattern recognition to systems identification, from learning control to biological modelling. In order to reflect the actual content of the book, the present title was selected. All the twenty-eight papers are roughly divided into two parts--Pattern Recognition and System Identification and Learning Process and Learning Control. It is sometimes quite obvious that some papers can be classified into either part. The choice in these cases was strictly the editor's in order to keep a certain balance between the two parts. During the past decade there has been a considerable growth of interest in problems of pattern recognition and machine learn ing. In designing an optimal pattern recognition or control system, if all the a priori information about the process under study is known and can be described deterministically, the optimal system is usually designed by deterministic optimization techniques.

Pattern Recognition

Pattern Recognition
Available:
Author: M. Narasimha Murty,V. Susheela Devi
Pages: 263
ISBN: 0857294954
Release: 2011-05-25
Editor: Springer Science & Business Media

DESCRIPTION OF THE BOOK:

Observing the environment and recognising patterns for the purpose of decision making is fundamental to human nature. This book deals with the scientific discipline that enables similar perception in machines through pattern recognition (PR), which has application in diverse technology areas. This book is an exposition of principal topics in PR using an algorithmic approach. It provides a thorough introduction to the concepts of PR and a systematic account of the major topics in PR besides reviewing the vast progress made in the field in recent times. It includes basic techniques of PR, neural networks, support vector machines and decision trees. While theoretical aspects have been given due coverage, the emphasis is more on the practical. The book is replete with examples and illustrations and includes chapter-end exercises. It is designed to meet the needs of senior undergraduate and postgraduate students of computer science and allied disciplines.

Mathematical Methodologies in Pattern Recognition and Machine Learning

Mathematical Methodologies in Pattern Recognition and Machine Learning
Available:
Author: Pedro Latorre Carmona,J. Salvador Sánchez,Ana L.N. Fred
Pages: 196
ISBN: 9781461450764
Release: 2012-11-09
Editor: Springer Science & Business Media

DESCRIPTION OF THE BOOK:

This volume features key contributions from the International Conference on Pattern Recognition Applications and Methods, (ICPRAM 2012,) held in Vilamoura, Algarve, Portugal from February 6th-8th, 2012. The conference provided a major point of collaboration between researchers, engineers and practitioners in the areas of Pattern Recognition, both from theoretical and applied perspectives, with a focus on mathematical methodologies. Contributions describe applications of pattern recognition techniques to real-world problems, interdisciplinary research, and experimental and theoretical studies which yield new insights that provide key advances in the field. This book will be suitable for scientists and researchers in optimization, numerical methods, computer science, statistics and for differential geometers and mathematical physicists.

Pattern Recognition Machine Intelligence and Biometrics

Pattern Recognition  Machine Intelligence and Biometrics
Available:
Author: Patrick S. P. Wang
Pages: 866
ISBN: 9783642224072
Release: 2012-02-13
Editor: Springer Science & Business Media

DESCRIPTION OF THE BOOK:

"Pattern Recognition, Machine Intelligence and Biometrics" covers the most recent developments in Pattern Recognition and its applications, using artificial intelligence technologies within an increasingly critical field. It covers topics such as: image analysis and fingerprint recognition; facial expressions and emotions; handwriting and signatures; iris recognition; hand-palm gestures; and multimodal based research. The applications span many fields, from engineering, scientific studies and experiments, to biomedical and diagnostic applications, to personal identification and homeland security. In addition, computer modeling and simulations of human behaviors are addressed in this collection of 31 chapters by top-ranked professionals from all over the world in the field of PR/AI/Biometrics. The book is intended for researchers and graduate students in Computer and Information Science, and in Communication and Control Engineering. Dr. Patrick S. P. Wang is a Professor Emeritus at the College of Computer and Information Science, Northeastern University, USA, Zijiang Chair of ECNU, Shanghai, and NSC Visiting Chair Professor of NTUST, Taipei.

Artificial Neural Networks and Statistical Pattern Recognition

Artificial Neural Networks and Statistical Pattern Recognition
Available:
Author: I.K. Sethi,Anil K Jain
Pages: 271
ISBN: 9781483297873
Release: 2014-06-28
Editor: Elsevier

DESCRIPTION OF THE BOOK:

With the growing complexity of pattern recognition related problems being solved using Artificial Neural Networks, many ANN researchers are grappling with design issues such as the size of the network, the number of training patterns, and performance assessment and bounds. These researchers are continually rediscovering that many learning procedures lack the scaling property; the procedures simply fail, or yield unsatisfactory results when applied to problems of bigger size. Phenomena like these are very familiar to researchers in statistical pattern recognition (SPR), where the curse of dimensionality is a well-known dilemma. Issues related to the training and test sample sizes, feature space dimensionality, and the discriminatory power of different classifier types have all been extensively studied in the SPR literature. It appears however that many ANN researchers looking at pattern recognition problems are not aware of the ties between their field and SPR, and are therefore unable to successfully exploit work that has already been done in SPR. Similarly, many pattern recognition and computer vision researchers do not realize the potential of the ANN approach to solve problems such as feature extraction, segmentation, and object recognition. The present volume is designed as a contribution to the greater interaction between the ANN and SPR research communities.

Pattern Recognition and Machine Intelligence

Pattern Recognition and Machine Intelligence
Available:
Author: Santanu Chaudhury,Sushmita Mitra,C.A. Murthy,P.S. Sastry,Sankar Kumar Pal
Pages: 631
ISBN: 9783642111631
Release: 2009-12-02
Editor: Springer Science & Business Media

DESCRIPTION OF THE BOOK:

This book constitutes the refereed proceedings of the Third International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009, held in New Delhi, India in December 2009. The 98 revised papers presented were carefully reviewed and selected from 221 initial submissions. The papers are organized in topical sections on pattern recognition and machine learning, soft computing andapplications, bio and chemo informatics, text and data mining, image analysis, document image processing, watermarking and steganography, biometrics, image and video retrieval, speech and audio processing, as well as on applications.

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
Available:
Author: Petra Perner
Pages: 614
ISBN: 9783642231988
Release: 2011-08-12
Editor: Springer Science & Business Media

DESCRIPTION OF THE BOOK:

This book constitutes the refereed proceedings of the 7th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2011, held in New York, NY, USA. The 44 revised full papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on classification and decision theory, theory of learning, clustering, application in medicine, webmining and information mining; and machine learning and image mining.