Supervised And Unsupervised Pattern Recognition

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Supervised and Unsupervised Pattern Recognition

Author : Evangelia Miche Tzanakou
Publisher : CRC Press
ISBN 13 : 1351835556
Total Pages : 441 pages
Book Rating : 4.3/5 (518 download)

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Book Synopsis Supervised and Unsupervised Pattern Recognition by : Evangelia Miche Tzanakou

Download or read book Supervised and Unsupervised Pattern Recognition written by Evangelia Miche Tzanakou and published by CRC Press. This book was released on 2017-12-19 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition. In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume.

Supervised and Unsupervised Pattern Recognition

Author : Evangelia Miche Tzanakou
Publisher : CRC Press
ISBN 13 : 1420049771
Total Pages : 392 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Supervised and Unsupervised Pattern Recognition by : Evangelia Miche Tzanakou

Download or read book Supervised and Unsupervised Pattern Recognition written by Evangelia Miche Tzanakou and published by CRC Press. This book was released on 2017-12-19 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition. In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume.

Supervised and Unsupervised Pattern Recognition

Author : Evangelia Miche Tzanakou
Publisher : CRC Press
ISBN 13 : 9780849322785
Total Pages : 392 pages
Book Rating : 4.3/5 (227 download)

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Book Synopsis Supervised and Unsupervised Pattern Recognition by : Evangelia Miche Tzanakou

Download or read book Supervised and Unsupervised Pattern Recognition written by Evangelia Miche Tzanakou and published by CRC Press. This book was released on 1999-12-28 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition. In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume.

Supervised and Unsupervised Learning for Data Science

Author : Michael W. Berry
Publisher : Springer Nature
ISBN 13 : 3030224759
Total Pages : 191 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Supervised and Unsupervised Learning for Data Science by : Michael W. Berry

Download or read book Supervised and Unsupervised Learning for Data Science written by Michael W. Berry and published by Springer Nature. This book was released on 2019-09-04 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

Supervised and Unsupervised Pattern Recognition

Author : Evangelia Miche Tzanakou
Publisher : CRC Press
ISBN 13 : 9781420049770
Total Pages : 396 pages
Book Rating : 4.0/5 (497 download)

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Book Synopsis Supervised and Unsupervised Pattern Recognition by : Evangelia Miche Tzanakou

Download or read book Supervised and Unsupervised Pattern Recognition written by Evangelia Miche Tzanakou and published by CRC Press. This book was released on 2017-12-19 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition. In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume.

Pattern Recognition and Classification

Author : Geoff Dougherty
Publisher : Springer Science & Business Media
ISBN 13 : 1461453232
Total Pages : 203 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Pattern Recognition and Classification by : Geoff Dougherty

Download or read book Pattern Recognition and Classification written by Geoff Dougherty and published by Springer Science & Business Media. This book was released on 2012-10-28 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Pattern Recognition

Author : Sergios Theodoridis
Publisher : Elsevier
ISBN 13 : 9780080513621
Total Pages : 689 pages
Book Rating : 4.5/5 (136 download)

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Book Synopsis Pattern Recognition by : Sergios Theodoridis

Download or read book Pattern Recognition written by Sergios Theodoridis and published by Elsevier. This book was released on 2003-05-15 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to "learn" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10 years of teaching experience, the text was developed by the authors through use in their own classrooms. *Approaches pattern recognition from the designer's point of view *New edition highlights latest developments in this growing field, including independent components and support vector machines, not available elsewhere *Supplemented by computer examples selected from applications of interest

Sampling Techniques for Supervised or Unsupervised Tasks

Author : Frédéric Ros
Publisher : Springer Nature
ISBN 13 : 3030293491
Total Pages : 232 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Sampling Techniques for Supervised or Unsupervised Tasks by : Frédéric Ros

Download or read book Sampling Techniques for Supervised or Unsupervised Tasks written by Frédéric Ros and published by Springer Nature. This book was released on 2019-10-26 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability and the “curse of dimensionality”, their implementations, evaluations, and applications. A large part of the book is dedicated to database comprising standard feature vectors, and a special section is reserved to the handling of more complex objects and dynamic scenarios. The book is ideal for anyone teaching or learning pattern recognition and interesting teaching or learning pattern recognition and is interested in the big data challenge. It provides an accessible introduction to the field and discusses the state of the art concerning sampling techniques for supervised and unsupervised task. Provides a comprehensive description of sampling techniques for unsupervised and supervised tasks; Describe implementation and evaluation of algorithms that simultaneously manage scalable problems and curse of dimensionality; Addresses the role of sampling in dynamic scenarios, sampling when dealing with complex objects, and new challenges arising from big data. "This book represents a timely collection of state-of-the art research of sampling techniques, suitable for anyone who wants to become more familiar with these helpful techniques for tackling the big data challenge." M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer Science, University of Central Arkansas "In science the difficulty is not to have ideas, but it is to make them work" From Carlo Rovelli

Face Image Analysis by Unsupervised Learning

Author : Marian Stewart Bartlett
Publisher : Springer Science & Business Media
ISBN 13 : 1461516374
Total Pages : 181 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Face Image Analysis by Unsupervised Learning by : Marian Stewart Bartlett

Download or read book Face Image Analysis by Unsupervised Learning written by Marian Stewart Bartlett and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.

Artificial Neural Networks in Pattern Recognition

Author : Friedhelm Schwenker
Publisher : Springer
ISBN 13 : 3642121594
Total Pages : 280 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Artificial Neural Networks in Pattern Recognition by : Friedhelm Schwenker

Download or read book Artificial Neural Networks in Pattern Recognition written by Friedhelm Schwenker and published by Springer. This book was released on 2010-04-16 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Neural Networks in Pattern Recognition synthesizes the proceedings of the 4th IAPR TC3 Workshop, ANNPR 2010. Topics include supervised and unsupervised learning, feature selection, pattern recognition in signal and image processing.

Advantages and Pitfalls of Pattern Recognition

Author : Horst Langer
Publisher : Elsevier
ISBN 13 : 0128118431
Total Pages : 350 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Advantages and Pitfalls of Pattern Recognition by : Horst Langer

Download or read book Advantages and Pitfalls of Pattern Recognition written by Horst Langer and published by Elsevier. This book was released on 2019-11-23 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advantages and Pitfalls of Pattern Recognition presents various methods of pattern recognition and classification, useful to geophysicists, geochemists, geologists, geographers, data analysts, and educators and students of geosciences. Scientific and technological progress has dramatically improved the knowledge of our planet with huge amounts of digital data available in various fields of Earth Sciences, such as geology, geophysics, and geography. This has led to a new perspective of data analysis, requiring specific techniques that take several features into consideration rather than single parameters. Pattern recognition techniques offer a suitable key for processing and extracting useful information from the data of multivariate analysis. This book explores both supervised and unsupervised pattern recognition techniques, while providing insight into their application. Offers real-world examples of techniques for pattern recognition and handling multivariate data Includes examples, applications, and diagrams to enhance understanding Provides an introduction and access to relevant software packages

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Author : Chris Aldrich
Publisher : Springer Science & Business Media
ISBN 13 : 1447151852
Total Pages : 374 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods by : Chris Aldrich

Download or read book Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods written by Chris Aldrich and published by Springer Science & Business Media. This book was released on 2013-06-15 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.

Unsupervised Learning Algorithms

Author : M. Emre Celebi
Publisher : Springer
ISBN 13 : 3319242113
Total Pages : 558 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Unsupervised Learning Algorithms by : M. Emre Celebi

Download or read book Unsupervised Learning Algorithms written by M. Emre Celebi and published by Springer. This book was released on 2016-04-29 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field.

Introduction to Pattern Recognition and Machine Learning

Author : M Narasimha Murty
Publisher : World Scientific
ISBN 13 : 9814656275
Total Pages : 404 pages
Book Rating : 4.8/5 (146 download)

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Book Synopsis Introduction to Pattern Recognition and Machine Learning by : M Narasimha Murty

Download or read book Introduction to Pattern Recognition and Machine Learning written by M Narasimha Murty and published by World Scientific. This book was released on 2015-04-22 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Handbook of Chemometrics and Qualimetrics

Author :
Publisher : Elsevier
ISBN 13 : 9780080887036
Total Pages : 876 pages
Book Rating : 4.8/5 (87 download)

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Book Synopsis Handbook of Chemometrics and Qualimetrics by :

Download or read book Handbook of Chemometrics and Qualimetrics written by and published by Elsevier. This book was released on 1998-12-04 with total page 876 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Statistical Pattern Recognition

Author : Andrew R. Webb
Publisher : John Wiley & Sons
ISBN 13 : 0470854782
Total Pages : 516 pages
Book Rating : 4.4/5 (78 download)

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Book Synopsis Statistical Pattern Recognition by : Andrew R. Webb

Download or read book Statistical Pattern Recognition written by Andrew R. Webb and published by John Wiley & Sons. This book was released on 2003-07-25 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition is a very active area of study andresearch, which has seen many advances in recent years. New andemerging applications - such as data mining, web searching,multimedia data retrieval, face recognition, and cursivehandwriting recognition - require robust and efficient patternrecognition techniques. Statistical decision making and estimationare regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fullyupdated with new methods, applications and references. It providesa comprehensive introduction to this vibrant area - with materialdrawn from engineering, statistics, computer science and the socialsciences - and covers many application areas, such as databasedesign, artificial neural networks, and decision supportsystems. * Provides a self-contained introduction to statistical patternrecognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vectormachines, and unsupervised classification. * Each section concludes with a description of the applicationsthat have been addressed and with further developments of thetheory. * Includes background material on dissimilarity, parameterestimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions tomore lengthy projects. The book is aimed primarily at senior undergraduate and graduatestudents studying statistical pattern recognition, patternprocessing, neural networks, and data mining, in both statisticsand engineering departments. It is also an excellent source ofreference for technical professionals working in advancedinformation development environments. For further information on the techniques and applicationsdiscussed in this book please visit ahref="http://www.statistical-pattern-recognition.net/"www.statistical-pattern-recognition.net/a

Pattern Recognition and Machine Learning

Author : Christopher M. Bishop
Publisher : Springer
ISBN 13 : 9781493938438
Total Pages : 0 pages
Book Rating : 4.9/5 (384 download)

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Book Synopsis Pattern Recognition and Machine Learning by : Christopher M. Bishop

Download or read book Pattern Recognition and Machine Learning written by Christopher M. Bishop and published by Springer. This book was released on 2016-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.