Big Data Analytics Cluster Analysis And Pattern Recognition Examples With Matlab
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Big Data Analytics Cluster Analysis And Pattern Recognition Examples With Matlab
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Author : PEREZ. C. PEREZ
language : en
Publisher:
Release Date : 2020
Big Data Analytics Cluster Analysis And Pattern Recognition Examples With Matlab written by PEREZ. C. PEREZ and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Big Data Analytics Cluster Analysis And Pattern Recognition Examples With Matlab
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Author : CESAR PEREZ LOPEZ
language : en
Publisher: SCIENTIFIC BOOKS
Release Date : 2020-05-31
Big Data Analytics Cluster Analysis And Pattern Recognition Examples With Matlab written by CESAR PEREZ LOPEZ and has been published by SCIENTIFIC BOOKS this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-31 with Computers categories.
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
Big Data Analytics Neural Networks Applications Examples With Matlab
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Author : CESAR PEREZ LOPEZ
language : en
Publisher: CESAR PEREZ
Release Date : 2020-05-31
Big Data Analytics Neural Networks Applications Examples With Matlab written by CESAR PEREZ LOPEZ and has been published by CESAR PEREZ this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-31 with Computers categories.
MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox since release 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 Parallel Computing Toolbox. This book develops neural network applications using MATLAB.
Chemometrics
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Author : Richard G. Brereton
language : en
Publisher: John Wiley & Sons
Release Date : 2018-05-29
Chemometrics written by Richard G. Brereton and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-29 with Science categories.
A new, full-color, completely updated edition of the key practical guide to chemometrics This new edition of this practical guide on chemometrics, emphasizes the principles and applications behind the main ideas in the field using numerical and graphical examples, which can then be applied to a wide variety of problems in chemistry, biology, chemical engineering, and allied disciplines. Presented in full color, it features expansion of the principal component analysis, classification, multivariate evolutionary signal and statistical distributions sections, and new case studies in metabolomics, as well as extensive updates throughout. Aimed at the large number of users of chemometrics, it includes extensive worked problems and chapters explaining how to analyze datasets, in addition to updated descriptions of how to apply Excel and Matlab for chemometrics. Chemometrics: Data Driven Extraction for Science, Second Edition offers chapters covering: experimental design, signal processing, pattern recognition, calibration, and evolutionary data. The pattern recognition chapter from the first edition is divided into two separate ones: Principal Component Analysis/Cluster Analysis, and Classification. It also includes new descriptions of Alternating Least Squares (ALS) and Iterative Target Transformation Factor Analysis (ITTFA). Updated descriptions of wavelets and Bayesian methods are included. Includes updated chapters of the classic chemometric methods (e.g. experimental design, signal processing, etc.) Introduces metabolomics-type examples alongside those from analytical chemistry Features problems at the end of each chapter to illustrate the broad applicability of the methods in different fields Supplemented with data sets and solutions to the problems on a dedicated website, www.booksupport.wiley.com Chemometrics: Data Driven Extraction for Science, Second Edition is recommended for post-graduate students of chemometrics as well as applied scientists (e.g. chemists, biochemists, engineers, statisticians) working in all areas of data analysis.
Exploratory Data Analysis With Matlab
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Author : Wendy L. Martinez
language : en
Publisher: CRC Press
Release Date : 2017-08-07
Exploratory Data Analysis With Matlab written by Wendy L. Martinez and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-07 with Mathematics categories.
Praise for the Second Edition: "The authors present an intuitive and easy-to-read book. ... accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB." —Adolfo Alvarez Pinto, International Statistical Review "Practitioners of EDA who use MATLAB will want a copy of this book. ... The authors have done a great service by bringing together so many EDA routines, but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA. —David A Huckaby, MAA Reviews Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity, EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models. Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book’s website. New to the Third Edition Random projections and estimating local intrinsic dimensionality Deep learning autoencoders and stochastic neighbor embedding Minimum spanning tree and additional cluster validity indices Kernel density estimation Plots for visualizing data distributions, such as beanplots and violin plots A chapter on visualizing categorical data
Proceedings Of International Conference On Computer Science And Information Technology
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Author : Srikanta Patnaik
language : en
Publisher: Springer Science & Business Media
Release Date : 2014-01-23
Proceedings Of International Conference On Computer Science And Information Technology written by Srikanta Patnaik and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-23 with Technology & Engineering categories.
The main objective of CSAIT 2013 is to provide a forum for researchers, educators, engineers and government officials involved in the general areas of Computational Sciences and Information Technology to disseminate their latest research results and exchange views on the future research directions of these fields. A medium like this provides an opportunity to the academicians and industrial professionals to exchange and integrate practice of computer science, application of the academic ideas, improve the academic depth. The in-depth discussions on the subject provide an international communication platform for educational technology and scientific research for the world's universities, engineering field experts, professionals and business executives.
Digital Signal Processing With Matlab Examples Volume 2
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Author : Jose Maria Giron-Sierra
language : en
Publisher: Springer
Release Date : 2016-12-02
Digital Signal Processing With Matlab Examples Volume 2 written by Jose Maria Giron-Sierra and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-02 with Technology & Engineering categories.
This is the second volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This second book focuses on recent developments in response to the demands of new digital technologies. It is divided into two parts: the first part includes four chapters on the decomposition and recovery of signals, with special emphasis on images. In turn, the second part includes three chapters and addresses important data-based actions, such as adaptive filtering, experimental modeling, and classification.
Production Planning And Control In Semiconductor Manufacturing
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Author : Tin-Chih Toly Chen
language : en
Publisher: Springer Nature
Release Date : 2022-09-19
Production Planning And Control In Semiconductor Manufacturing written by Tin-Chih Toly Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-19 with Technology & Engineering categories.
This book systematically analyzes the applicability of big data analytics and Industry 4.0 from the perspective of semiconductor manufacturing management. It reports in real examples and presents case studies as supporting evidence. In recent years, technologies of big data analytics and Industry 4.0 have been frequently applied to the management of semiconductor manufacturing. However, related research results are mostly scattered in various journal issues or conference proceedings, and there is an urgent need for a systematic integration of these results. In addition, many related discussions have placed too much emphasis on the theoretical framework of information systems rather than on the needs of semiconductor manufacturing management. This book addresses these issues.
From Maps To Models
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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2016-10-31
From Maps To Models written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-31 with Science categories.
The United States faces numerous, varied, and evolving threats to national security, including terrorism, scarcity and disruption of food and water supplies, extreme weather events, and regional conflicts around the world. Effectively managing these threats requires intelligence that not only assesses what is happening now, but that also anticipates potential future threats. The National Geospatial-Intelligence Agency (NGA) is responsible for providing geospatial intelligence on other countriesâ€"assessing where exactly something is, what it is, and why it is importantâ€"in support of national security, disaster response, and humanitarian assistance. NGA's approach today relies heavily on imagery analysis and mapping, which provide an assessment of current and past conditions. However, augmenting that approach with a strong modeling capability would enable NGA to also anticipate and explore future outcomes. A model is a simplified representation of a real-world system that is used to extract explainable insights about the system, predict future outcomes, or explore what might happen under plausible what-if scenarios. Such models use data and/or theory to specify inputs (e.g., initial conditions, boundary conditions, and model parameters) to produce an output. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities describes the types of models and analytical methods used to understand real-world systems, discusses what would be required to make these models and methods useful for geospatial intelligence, and identifies supporting research and development for NGA. This report provides examples of models that have been used to help answer the sorts of questions NGA might ask, describes how to go about a model-based investigation, and discusses models and methods that are relevant to NGA's mission.
Unsupervised Learning
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Author : Matthew Kyan
language : en
Publisher: John Wiley & Sons
Release Date : 2014-05-02
Unsupervised Learning written by Matthew Kyan and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-02 with Computers categories.
A new approach to unsupervised learning Evolving technologies have brought about an explosion of information in recent years, but the question of how such information might be effectively harvested, archived, and analyzed remains a monumental challenge—for the processing of such information is often fraught with the need for conceptual interpretation: a relatively simple task for humans, yet an arduous one for computers. Inspired by the relative success of existing popular research on self-organizing neural networks for data clustering and feature extraction, Unsupervised Learning: A Dynamic Approach presents information within the family of generative, self-organizing maps, such as the self-organizing tree map (SOTM) and the more advanced self-organizing hierarchical variance map (SOHVM). It covers a series of pertinent, real-world applications with regard to the processing of multimedia data—from its role in generic image processing techniques, such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data. Self-organization concepts and applications discussed include: Distance metrics for unsupervised clustering Synaptic self-amplification and competition Image retrieval Impulse noise removal Microbiological image analysis Unsupervised Learning: A Dynamic Approach introduces a new family of unsupervised algorithms that have a basis in self-organization, making it an invaluable resource for researchers, engineers, and scientists who want to create systems that effectively model oppressive volumes of data with little or no user intervention.