Algorithms And Applications
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Deep Learning Algorithms And Applications
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Author : Witold Pedrycz
language : en
Publisher: Springer Nature
Release Date : 2019-10-23
Deep Learning Algorithms And Applications written by Witold Pedrycz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-23 with Technology & Engineering categories.
This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
Machine Learning
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Author : Mohssen Mohammed
language : en
Publisher: CRC Press
Release Date : 2016-08-19
Machine Learning written by Mohssen Mohammed and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-19 with Computers categories.
Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.
Digraphs
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Author : Jorgen Bang-Jensen
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Digraphs written by Jorgen Bang-Jensen 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 2013-06-29 with Mathematics categories.
Graph theory is a very popular area of discrete mathematics with not only numerous theoretical developments, but also countless applications to prac tical problems. As a research area, graph theory is still relatively young, but it is maturing rapidly with many deep results having been discovered over the last couple of decades. The theory of graphs can be roughly partitioned into two branches: the areas of undirected graphs and directed graphs (digraphs). Even though both areas have numerous important applications, for various reasons, undirected graphs have been studied much more extensively than directed graphs. One of the reasons is that undirected graphs form in a sense a special class of directed graphs (symmetric digraphs) and hence problems that can be for mulated for both directed and undirected graphs are often easier for the latter. Another reason is that, unlike for the case of undirected graphs, for which there are several important books covering both classical and recent results, no previous book covers more than a small fraction of the results obtained on digraphs within the last 25 years. Typically, digraphs are consid ered only in one chapter or by a few elementary results scattered throughout the book. Despite all this, the theory of directed graphs has developed enormously within the last three decades. There is an extensive literature on digraphs (more than 3000 papers). Many of these papers contain, not only interesting theoretical results, but also important algorithms as well as applications.
Monte Carlo
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Author : George Fishman
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Monte Carlo written by George Fishman 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 2013-03-09 with Mathematics categories.
This book provides an introduction to the Monte Carlo method suitable for a one-or two-semester course for graduate and advanced undergraduate students in the mathematical and engineering sciences. It also can serve as a reference for the professional analyst. In the past, my inability to provide students with a single source book on this topic for class and for later professional reference had left me repeatedly frustrated, and eventually motivated me to write this book. In addition to focused accounts of major topics, the book has two unifying themes: One concerns the effective use of information and the other concerns error control and reduction. The book describes how to incorporate information about a problem into a sampling plan in a way that reduces the cost of estimating its solution to within a specified error bound. Although exploiting special structures to reduce cost long has been a hallmark of the Monte Carlo method, the propen sity of users of the method to discard useful information because it does not fit traditional textbook models repeatedly has impressed me. The present account aims at reducing the impediments to integrating this information. Errors, both statistical and computational, abound in every Monte Carlo sam pling experiment, and a considerable methodology exists for controlling them.
Machine Learning Algorithms And Applications
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Author : Mettu Srinivas
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-10
Machine Learning Algorithms And Applications written by Mettu Srinivas 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 2021-08-10 with Computers categories.
Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.
Stochastic Approximation And Recursive Algorithms And Applications
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Author : Harold Kushner
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-04
Stochastic Approximation And Recursive Algorithms And Applications written by Harold Kushner 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 2006-05-04 with Mathematics categories.
The basic stochastic approximation algorithms introduced by Robbins and MonroandbyKieferandWolfowitzintheearly1950shavebeenthesubject of an enormous literature, both theoretical and applied. This is due to the large number of applications and the interesting theoretical issues in the analysis of “dynamically de?ned” stochastic processes. The basic paradigm is a stochastic di?erence equation such as ? = ? + Y , where ? takes n+1 n n n n its values in some Euclidean space, Y is a random variable, and the “step n size” > 0 is small and might go to zero as n??. In its simplest form, n ? is a parameter of a system, and the random vector Y is a function of n “noise-corrupted” observations taken on the system when the parameter is set to ? . One recursively adjusts the parameter so that some goal is met n asymptotically. Thisbookisconcernedwiththequalitativeandasymptotic properties of such recursive algorithms in the diverse forms in which they arise in applications. There are analogous continuous time algorithms, but the conditions and proofs are generally very close to those for the discrete time case. The original work was motivated by the problem of ?nding a root of a continuous function g ̄(?), where the function is not known but the - perimenter is able to take “noisy” measurements at any desired value of ?. Recursive methods for root ?nding are common in classical numerical analysis, and it is reasonable to expect that appropriate stochastic analogs would also perform well.
Data Classification
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Author : Charu C. Aggarwal
language : en
Publisher: CRC Press
Release Date : 2014-07-25
Data Classification written by Charu C. Aggarwal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-25 with Business & Economics categories.
Comprehensive Coverage of the Entire Area of Classification Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. This comprehensive book focuses on three primary aspects of data classification: Methods: The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. Domains: The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. Variations: The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.
Gps
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Author : Guochang Xu
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-05
Gps written by Guochang Xu 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 2007-10-05 with Science categories.
This, the second edition of the hugely practical reference and handbook describes kinematic, static and dynamic Global Positioning System theory and applications. It is primarily based upon source-code descriptions of the KSGSoft program developed by the author and his colleagues and used in the AGMASCO project of the EU. This is the first book to report the unified GPS data processing method and algorithm that uses equations for selectively eliminated equivalent observations.
Computational Geometry
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Author : Mark de Berg
language : en
Publisher: Springer Science & Business Media
Release Date : 2000
Computational Geometry written by Mark de Berg 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 2000 with Computers categories.
For students this motivation will be especially welcome.
Image Based Rendering
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Author : Heung-Yeung Shum
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-05-26
Image Based Rendering written by Heung-Yeung Shum 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 2008-05-26 with Computers categories.
Image-Based Rendering examines the theory, practice, and applications associated with image-based rendering and modeling. As leading researchers in the field, the authors combine their unique experiences in computer graphics, computer vision, and signal processing to address the multidisciplinary nature of IBR research. The topics covered vary from IBR basic concepts and representations on the theory side to signal processing and data compression on the practical side. Several IBR systems built to-date are examined. Rather than focusing on 3D modeling aspects, which have been extensively treated elsewhere in the vision literature, the book focuses primarily on IBR. One of the only titles devoted exclusively to IBR, this book is intended for researchers, professionals, and general readers interested in the topics of computer graphics, computer vision, image process, and video processing. Advanced-level students in EECS studying related disciplines will be able to seriously expand their knowledge about image-based rendering.