Pattern Recognition And Big Data

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Pattern Recognition And Big Data

Author : Pal Sankar Kumar
Publisher : World Scientific
ISBN 13 : 9813144564
Total Pages : 876 pages
Book Rating : 4.8/5 (131 download)

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Book Synopsis Pattern Recognition And Big Data by : Pal Sankar Kumar

Download or read book Pattern Recognition And Big Data written by Pal Sankar Kumar and published by World Scientific. This book was released on 2016-12-15 with total page 876 pages. Available in PDF, EPUB and Kindle. Book excerpt: Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications. Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.

Machine Learning and Big Data

Author : Uma N. Dulhare
Publisher : John Wiley & Sons
ISBN 13 : 1119654742
Total Pages : 544 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Machine Learning and Big Data by : Uma N. Dulhare

Download or read book Machine Learning and Big Data written by Uma N. Dulhare and published by John Wiley & Sons. This book was released on 2020-09-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning and Data Mining in Pattern Recognition

Author : Petra Perner
Publisher : Springer
ISBN 13 : 3319624164
Total Pages : 452 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Machine Learning and Data Mining in Pattern Recognition by : Petra Perner

Download or read book Machine Learning and Data Mining in Pattern Recognition written by Petra Perner and published by Springer. This book was released on 2017-07-01 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017, held in New York, NY, USA in July/August 2017.The 31 full papers presented in this book were carefully reviewed and selected from 150 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.

Big Data: Conceptual Analysis and Applications

Author : Michael Z. Zgurovsky
Publisher : Springer
ISBN 13 : 3030142981
Total Pages : 277 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Big Data: Conceptual Analysis and Applications by : Michael Z. Zgurovsky

Download or read book Big Data: Conceptual Analysis and Applications written by Michael Z. Zgurovsky and published by Springer. This book was released on 2019-03-20 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used. Application of hybrid neuro-fuzzy networks for analyzing stock markets was presented. The analysis of big historical, economic and physical data revealed the hidden Fibonacci pattern about the course of systemic world conflicts and their connection with the Kondratieff big economic cycles and the Schwabe–Wolf solar activity cycles. The book is useful for system analysts and practitioners working with complex systems in various spheres of human activity.

Pattern Recognition and Data Analysis with Applications

Author : Deepak Gupta
Publisher : Springer Nature
ISBN 13 : 9811915202
Total Pages : 816 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Pattern Recognition and Data Analysis with Applications by : Deepak Gupta

Download or read book Pattern Recognition and Data Analysis with Applications written by Deepak Gupta and published by Springer Nature. This book was released on 2022-09-01 with total page 816 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing and their applications in real world. The topics covered in machine learning involves feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN) and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modelling from video, 3D object recognition, localization and tracking, medical image analysis and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multi-task, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG) and electromyogram (EMG).

Internet-Scale Pattern Recognition

Author : Anang Hudaya Muhamad Amin
Publisher : CRC Press
ISBN 13 : 1466510978
Total Pages : 197 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Internet-Scale Pattern Recognition by : Anang Hudaya Muhamad Amin

Download or read book Internet-Scale Pattern Recognition written by Anang Hudaya Muhamad Amin and published by CRC Press. This book was released on 2012-11-20 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: For machine intelligence applications to work successfully, machines must perform reliably under variations of data and must be able to keep up with data streams. Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds unveils computational models that address performance and scalability to achieve higher levels of reliability. It explores different ways of implementing pattern recognition using machine intelligence. Based on the authors’ research from the past 10 years, the text draws on concepts from pattern recognition, parallel processing, distributed systems, and data networks. It describes fundamental research on the scalability and performance of pattern recognition, addressing issues with existing pattern recognition schemes for Internet-scale data deployment. The authors review numerous approaches and introduce possible solutions to the scalability problem. By presenting the concise body of knowledge required for reliable and scalable pattern recognition, this book shortens the learning curve and gives you valuable insight to make further innovations. It offers an extendable template for Internet-scale pattern recognition applications as well as guidance on the programming of large networks of devices.

BIG DATA ANALYTICS: CLUSTER ANALYSIS AND PATTERN RECOGNITION. EXAMPLES WITH MATLAB

Author : PEREZ. C. PEREZ
Publisher :
ISBN 13 : 9781716875823
Total Pages : 0 pages
Book Rating : 4.8/5 (758 download)

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Book Synopsis BIG DATA ANALYTICS: CLUSTER ANALYSIS AND PATTERN RECOGNITION. EXAMPLES WITH MATLAB by : PEREZ. C. PEREZ

Download or read book BIG DATA ANALYTICS: CLUSTER ANALYSIS AND PATTERN RECOGNITION. EXAMPLES WITH MATLAB written by PEREZ. C. PEREZ and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Big Data Analytics

Author : C. Perez
Publisher : CESAR PEREZ
ISBN 13 : 1716876869
Total Pages : 389 pages
Book Rating : 4.7/5 (168 download)

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Book Synopsis Big Data Analytics by : C. Perez

Download or read book Big Data Analytics written by C. Perez and published by CESAR PEREZ. This book was released on 2020-05-31 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox from version 18) that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Big Data tools (Parallel Computing Toolbox). Unsupervised learning algorithms, including self-organizing maps and competitive layers-Apps for data-fitting, pattern recognition, and clustering-Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance. his book develops cluster analysis and pattern recognition

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.

Pattern Recognition Algorithms for Data Mining

Author : Sankar K. Pal
Publisher : CRC Press
ISBN 13 : 1135436401
Total Pages : 275 pages
Book Rating : 4.1/5 (354 download)

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Book Synopsis Pattern Recognition Algorithms for Data Mining by : Sankar K. Pal

Download or read book Pattern Recognition Algorithms for Data Mining written by Sankar K. Pal and published by CRC Press. This book was released on 2004-05-27 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

Pattern Recognition: From Classical To Modern Approaches

Author : Sankar Kumar Pal
Publisher : World Scientific
ISBN 13 : 9814490636
Total Pages : 635 pages
Book Rating : 4.8/5 (144 download)

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Book Synopsis Pattern Recognition: From Classical To Modern Approaches by : Sankar Kumar Pal

Download or read book Pattern Recognition: From Classical To Modern Approaches written by Sankar Kumar Pal and published by World Scientific. This book was released on 2001-11-23 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume, containing contributions by experts from all over the world, is a collection of 21 articles which present review and research material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, syntactic/linguistic, fuzzy-set-theoretic, neural, genetic-algorithmic and rough-set-theoretic to hybrid soft computing, with significant real-life applications. In addition, the book describes efficient soft machine learning algorithms for data mining and knowledge discovery. With a balanced mixture of theory, algorithms and applications, as well as up-to-date information and an extensive bibliography, Pattern Recognition: From Classical to Modern Approaches is a very useful resource.

Pattern Recognition and Machine Intelligence

Author : Marzena Kryszkiewicz
Publisher : Springer
ISBN 13 : 3319199412
Total Pages : 588 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Pattern Recognition and Machine Intelligence by : Marzena Kryszkiewicz

Download or read book Pattern Recognition and Machine Intelligence written by Marzena Kryszkiewicz and published by Springer. This book was released on 2015-06-22 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 6th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2015, held in Warsaw, Poland, in June/July 2015. The total of 53 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 90 submissions. They were organized in topical sections named: foundations of machine learning; image processing; image retrieval; image tracking; pattern recognition; data mining techniques for large scale data; fuzzy computing; rough sets; bioinformatics; and applications of artificial intelligence.

Pattern Recognition

Author : Wladyslaw Homenda
Publisher : John Wiley & Sons
ISBN 13 : 1119302854
Total Pages : 320 pages
Book Rating : 4.1/5 (193 download)

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Book Synopsis Pattern Recognition by : Wladyslaw Homenda

Download or read book Pattern Recognition written by Wladyslaw Homenda and published by John Wiley & Sons. This book was released on 2018-02-09 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new approach to the issue of data quality in pattern recognition Detailing foundational concepts before introducing more complex methodologies and algorithms, this book is a self-contained manual for advanced data analysis and data mining. Top-down organization presents detailed applications only after methodological issues have been mastered, and step-by-step instructions help ensure successful implementation of new processes. By positioning data quality as a factor to be dealt with rather than overcome, the framework provided serves as a valuable, versatile tool in the analysis arsenal. For decades, practical need has inspired intense theoretical and applied research into pattern recognition for numerous and diverse applications. Throughout, the limiting factor and perpetual problem has been data—its sheer diversity, abundance, and variable quality presents the central challenge to pattern recognition innovation. Pattern Recognition: A Quality of Data Perspective repositions that challenge from a hurdle to a given, and presents a new framework for comprehensive data analysis that is designed specifically to accommodate problem data. Designed as both a practical manual and a discussion about the most useful elements of pattern recognition innovation, this book: Details fundamental pattern recognition concepts, including feature space construction, classifiers, rejection, and evaluation Provides a systematic examination of the concepts, design methodology, and algorithms involved in pattern recognition Includes numerous experiments, detailed schemes, and more advanced problems that reinforce complex concepts Acts as a self-contained primer toward advanced solutions, with detailed background and step-by-step processes Introduces the concept of granules and provides a framework for granular computing Pattern recognition plays a pivotal role in data analysis and data mining, fields which are themselves being applied in an expanding sphere of utility. By facing the data quality issue head-on, this book provides students, practitioners, and researchers with a clear way forward amidst the ever-expanding data supply.

Machine Learning Paradigms

Author : Maria Virvou
Publisher : Springer
ISBN 13 : 3030137430
Total Pages : 223 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Machine Learning Paradigms by : Maria Virvou

Download or read book Machine Learning Paradigms written by Maria Virvou and published by Springer. This book was released on 2019-03-16 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.

The Phantom Pattern Problem

Author : Gary Smith
Publisher : Oxford University Press, USA
ISBN 13 : 0198864167
Total Pages : 236 pages
Book Rating : 4.1/5 (988 download)

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Book Synopsis The Phantom Pattern Problem by : Gary Smith

Download or read book The Phantom Pattern Problem written by Gary Smith and published by Oxford University Press, USA. This book was released on 2020-08-18 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Patterns in data are often used as evidence, but how can you tell if that evidence is worth believing? The Phantom Pattern Problem helps readers avoid being duped by data, tricked into worthless investing strategies, or scared out of getting vaccinations. Becoming a sceptical consumer of data is important in this age of Big Data.

Process Mining Techniques for Pattern Recognition

Author : Vikash Yadav
Publisher : CRC Press
ISBN 13 : 100054057X
Total Pages : 180 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Process Mining Techniques for Pattern Recognition by : Vikash Yadav

Download or read book Process Mining Techniques for Pattern Recognition written by Vikash Yadav and published by CRC Press. This book was released on 2022-02-28 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the theory, practice, and concepts of process mining techniques in detail, especially pattern recognition in diverse society, science, medicine, engineering, and business. The book deliberates several perspectives on process mining techniques in the broader context of data science and big data approaches. Process Mining Techniques for Pattern Recognition: Concepts, Theory, and Practice provides an introduction to process mining techniques and pattern recognition. After that, it delivers the fundamentals of process modelling and mining essential to comprehend the book. The text emphasizes discovery as an important process mining task and includes case studies as well as real-life examples to guide users in successfully applying process mining techniques for pattern recognition in practice. Intended to be an introduction to process mining and pattern recognition for students, academics, and practitioners, this book is perfect for those who want to learn the basics, and also gain an understanding of the concepts on a deeper level.

Introduction to Pattern Recognition

Author : Sergios Theodoridis
Publisher : Academic Press
ISBN 13 : 9780080922751
Total Pages : 231 pages
Book Rating : 4.9/5 (227 download)

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Book Synopsis Introduction to Pattern Recognition by : Sergios Theodoridis

Download or read book Introduction to Pattern Recognition written by Sergios Theodoridis and published by Academic Press. This book was released on 2010-03-03 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition. It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision. Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth Edition Solved examples in Matlab, including real-life data sets in imaging and audio recognition Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)