Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
Author: Christopher M. Bishop
Publsiher: Springer
Total Pages: 738
Release: 2016-08-23
ISBN 10: 9781493938438
ISBN 13: 1493938436
Language: EN, FR, DE, ES & NL

Pattern Recognition and Machine Learning Book Review:

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
Author: Y. Anzai
Publsiher: Elsevier
Total Pages: 407
Release: 2012-12-02
ISBN 10: 0080513638
ISBN 13: 9780080513638
Language: EN, FR, DE, ES & NL

Pattern Recognition and Machine Learning Book Review:

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.

Sequential Methods in Pattern Recognition and Machine Learning

Sequential Methods in Pattern Recognition and Machine Learning
Author: K.C. Fu
Publsiher: Academic Press
Total Pages: 226
Release: 1968
ISBN 10: 0080955592
ISBN 13: 9780080955599
Language: EN, FR, DE, ES & NL

Sequential Methods in Pattern Recognition and Machine Learning Book Review:

Sequential Methods in Pattern Recognition and Machine Learning

Pattern Recognition and Machine Intelligence

Pattern Recognition and Machine Intelligence
Author: B. Uma Shankar,Kuntal Ghosh,Deba Prasad Mandal,Shubhra Sankar Ray,David Zhang,Sankar K. Pal
Publsiher: Springer
Total Pages: 695
Release: 2017-12-06
ISBN 10: 3319699008
ISBN 13: 9783319699004
Language: EN, FR, DE, ES & NL

Pattern Recognition and Machine Intelligence Book Review:

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.

Introduction to Pattern Recognition and Machine Learning

Introduction to Pattern Recognition and Machine Learning
Author: M Narasimha Murty,V Susheela Devi
Publsiher: World Scientific
Total Pages: 404
Release: 2015-04-22
ISBN 10: 9814656275
ISBN 13: 9789814656276
Language: EN, FR, DE, ES & NL

Introduction to Pattern Recognition and Machine Learning Book Review:

This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics — neural networks, support vector machines and decision trees — attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior computer science undergraduates, with a deeper understanding of the subject matter. Contents:IntroductionTypes of DataFeature Extraction and Feature SelectionBayesian LearningClassificationClassification Using Soft Computing TechniquesData ClusteringSoft ClusteringApplication — Social and Information Networks Readership: Academics and working professionals in computer science. Key Features:The algorithmic approach taken and the practical issues dealt with will aid the reader in writing programs and implementing methodsCovers recent and advanced topics by providing working exercises, examples and illustrations in each chapterProvides the reader with a deeper understanding of the subject matterKeywords:Clustering;Classification;Supervised Learning;Soft Computing

Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Author: Brian D. Ripley
Publsiher: Cambridge University Press
Total Pages: 403
Release: 2007
ISBN 10: 9780521717700
ISBN 13: 0521717701
Language: EN, FR, DE, ES & NL

Pattern Recognition and Neural Networks Book Review:

Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
Author: N.A
Publsiher:
Total Pages: 365
Release: 2018-05
ISBN 10: 9781642241525
ISBN 13: 1642241520
Language: EN, FR, DE, ES & NL

Pattern Recognition and Machine Learning Book Review:

Pattern recognition is persistent to be one of the imperative research fields in computer science and electrical engineering. Plenty of new applications are rising, and consequently pattern analysis and synthesis turn into significant subfields in pattern recognition. In these days, giving a computer to carry out any task involve a set of specific instructions or the accomplishment of an algorithm that defines the rules that need to be followed. The present day computer system has no ability to learn from past experiences and hence cannot readily recover on the basis of past mistakes. Subsequently, giving a computer or instructing a computer controlled program to execute a task entail one to define an absolute and accurate algorithm for task and then program the algorithm into the computer. Research in machine learning is now converging from several sources and from artificial intelligent field.This book as the name suggests Pattern Recognition and Machine Learning is packed with the benefits of machine learning and pattern recognition techniques and research in machine learning. The book covers chapters that aim to realize the future abilities by presenting a variety of integrated research in various scientific and engineering fields such as perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and also will be of helpful for graduate students, researchers, and practicing engineers working in the field of machine vision and computer science and engineering.

Pattern Recognition, Machine Intelligence and Biometrics

Pattern Recognition, Machine Intelligence and Biometrics
Author: Patrick S. P. Wang
Publsiher: Springer Science & Business Media
Total Pages: 866
Release: 2012-02-13
ISBN 10: 3642224075
ISBN 13: 9783642224072
Language: EN, FR, DE, ES & NL

Pattern Recognition, Machine Intelligence and Biometrics Book Review:

"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.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
Author: King-Sun Fu
Publsiher: Springer
Total Pages: 343
Release: 1971-07
ISBN 10:
ISBN 13: WISC:89037590429
Language: EN, FR, DE, ES & NL

Pattern Recognition and Machine Learning Book Review:

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
Author: Christopher M. Bishop
Publsiher: Springer Verlag
Total Pages: 738
Release: 2006-08-17
ISBN 10: 9780387310732
ISBN 13: 0387310738
Language: EN, FR, DE, ES & NL

Pattern Recognition and Machine Learning Book Review:

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 Intelligence

Pattern Recognition and Machine Intelligence
Author: Santanu Chaudhury,Sushmita Mitra,C.A. Murthy,P.S. Sastry,Sankar Kumar Pal
Publsiher: Springer Science & Business Media
Total Pages: 631
Release: 2009-12-02
ISBN 10: 3642111637
ISBN 13: 9783642111631
Language: EN, FR, DE, ES & NL

Pattern Recognition and Machine Intelligence Book Review:

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 Automatic Pattern Recognition

Machine Learning and Automatic Pattern Recognition
Author: David John Braverman
Publsiher:
Total Pages: 166
Release: 1961
ISBN 10:
ISBN 13: WISC:89033940008
Language: EN, FR, DE, ES & NL

Machine Learning and Automatic Pattern Recognition Book Review:

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Author: Christopher M. Bishop
Publsiher: Oxford University Press
Total Pages: 482
Release: 1995-11-23
ISBN 10: 0198538642
ISBN 13: 9780198538646
Language: EN, FR, DE, ES & NL

Neural Networks for Pattern Recognition Book Review:

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

Machine Learning and Automatic Pattern Recognition

Machine Learning and Automatic Pattern Recognition
Author: Stanford University Stanford Electronics Laboratories
Publsiher:
Total Pages: 83
Release: 1961
ISBN 10:
ISBN 13: STANFORD:36105046371022
Language: EN, FR, DE, ES & NL

Machine Learning and Automatic Pattern Recognition Book Review:

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
Author: Petra Perner,Maria Petrou
Publsiher: Springer Science & Business Media
Total Pages: 224
Release: 1999-09-08
ISBN 10: 9783540665991
ISBN 13: 3540665994
Language: EN, FR, DE, ES & NL

Machine Learning and Data Mining in Pattern Recognition Book Review:

The field of machine learning and data mining in connection with pattern recognition enjoys growing popularity and attracts many researchers. Automatic pattern recognition systems have proven successful in many applications. The wide use of these systems depends on their ability to adapt to changing environmental conditions and to deal with new objects. This requires learning capabilities on the parts of these systems. The exceptional attraction of learning in pattern recognition lies in the specific data themselves and the different stages at which they get processed in a pattern recognition system. This results a specific branch within the field of machine learning. At the workshop, were presented machine learning approaches for image pre-processing, image segmentation, recognition and interpretation. Machine learning systems were shown on applications such as document analysis and medical image analysis. Many databases are developed that contain multimedia sources such as images, measurement protocols, and text documents. Such systems should be able to retrieve these sources by content. That requires specific retrieval and indexing strategies for images and signals. Higher quality database contents can be achieved if it were possible to mine these databases for their underlying information. Such mining techniques have to consider the specific characteristic of the image sources. The field of mining multimedia databases is just starting out. We hope that our workshop can attract many other researchers to this subject.

Pattern Recognition and Classification

Pattern Recognition and Classification
Author: Geoff Dougherty
Publsiher: Springer Science & Business Media
Total Pages: 196
Release: 2012-10-28
ISBN 10: 1461453232
ISBN 13: 9781461453239
Language: EN, FR, DE, ES & NL

Pattern Recognition and Classification Book Review:

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.

Fundamentals of Pattern Recognition and Machine Learning

Fundamentals of Pattern Recognition and Machine Learning
Author: Ulisses Braga-Neto
Publsiher: Springer
Total Pages: 357
Release: 2020-11-01
ISBN 10: 9783030276553
ISBN 13: 3030276554
Language: EN, FR, DE, ES & NL

Fundamentals of Pattern Recognition and Machine Learning Book Review:

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.

Machine Learning Techniques for Pattern Recognition and Information Security

Machine Learning Techniques for Pattern Recognition and Information Security
Author: Mohit Dua,Ankit Kumar Jain
Publsiher: Engineering Science Reference
Total Pages: 300
Release: 2020
ISBN 10: 9781799832997
ISBN 13: 1799832996
Language: EN, FR, DE, ES & NL

Machine Learning Techniques for Pattern Recognition and Information Security Book Review:

"This book examines the impact of machine learning techniques on pattern recognition and information security"--

Pattern Recognition and Machine Intelligence

Pattern Recognition and Machine Intelligence
Author: Pradipta Maji,Ashish Ghosh,M. Narasimha Murty,Kuntal Ghosh,Sankar K. Pal
Publsiher: Springer
Total Pages: 753
Release: 2013-12-09
ISBN 10: 3642450628
ISBN 13: 9783642450624
Language: EN, FR, DE, ES & NL

Pattern Recognition and Machine Intelligence Book Review:

This book constitutes the refereed proceedings of the 5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013, held in Kolkata, India in December 2013. The 101 revised papers presented together with 9 invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on pattern recognition; machine learning; image processing; speech and video processing; medical imaging; document image processing; soft computing; bioinformatics and computational biology; and social media mining.

A First Course in Machine Learning

A First Course in Machine Learning
Author: Simon Rogers,Mark Girolami
Publsiher: CRC Press
Total Pages: 397
Release: 2016-10-14
ISBN 10: 1498738540
ISBN 13: 9781498738545
Language: EN, FR, DE, ES & NL

A First Course in Machine Learning Book Review:

"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC." —Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade." —Daniel Barbara, George Mason University, Fairfax, Virginia, USA "The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts." —Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength...Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." —David Clifton, University of Oxford, UK "The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." —Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK "This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning...The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective." —Guangzhi Qu, Oakland University, Rochester, Michigan, USA