Unsupervised Classification
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Unsupervised Classification
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Author : Sanghamitra Bandyopadhyay
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-13
Unsupervised Classification written by Sanghamitra Bandyopadhyay 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 2012-12-13 with Computers categories.
Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature. This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection. The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.
Unsupervised Learning Algorithms
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Author : M. Emre Celebi
language : en
Publisher: Springer
Release Date : 2016-05-09
Unsupervised Learning Algorithms written by M. Emre Celebi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-09 with Technology & Engineering categories.
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.
The Effect Of Training Block Size On Unsupervised Classification Of Landsat Thematic Mapper Imagery
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Author : Paul W. Snook
language : en
Publisher:
Release Date : 1991
The Effect Of Training Block Size On Unsupervised Classification Of Landsat Thematic Mapper Imagery written by Paul W. Snook and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Remote sensing categories.
Manual Of Geospatial Science And Technology
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Author : John D. Bossler
language : en
Publisher: CRC Press
Release Date : 2010-03-05
Manual Of Geospatial Science And Technology written by John D. Bossler and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-03-05 with Technology & Engineering categories.
Following in the tradition of its popular predecessor, the Manual of Geospatial Science and Technology, Second Edition continues to be the authoritative volume that covers all aspects of the field, both basic and applied, and includes a focus on initiating, planning, and managing GIS projects. This comprehensive resource, which contains contributio
Hands On Unsupervised Learning Using Python
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Author : Ankur A. Patel
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-02-21
Hands On Unsupervised Learning Using Python written by Ankur A. Patel and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with Computers categories.
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks
Multispectral Image Analysis Using The Object Oriented Paradigm
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Author : Kumar Navulur
language : en
Publisher: CRC Press
Release Date : 2006-12-05
Multispectral Image Analysis Using The Object Oriented Paradigm written by Kumar Navulur and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-12-05 with Technology & Engineering categories.
Bringing a fresh new perspective to remote sensing, object-based image analysis is a paradigm shift from the traditional pixel-based approach. Featuring various practical examples to provide understanding of this new modus operandi, Multispectral Image Analysis Using the Object-Oriented Paradigm reviews the current image analysis methods and demonstrates advantages to improve information extraction from imagery. This reference describes traditional image analysis techniques, introduces object-oriented technology, and discusses the benefits of object-based versus pixel-based classification. It examines the creation of object primitives using image segmentation approaches and the use of various techniques for object classification. The author covers image enhancement methods, how to use ancillary data to constrain image segmentation, and concepts of semantic grouping of objects. He concludes by addressing accuracy assessment approaches. The accompanying downloadable resources present sample data that enable the use of different approaches to problem solving. Integrating remote sensing techniques and GIS analysis, Multispectral Image Analysis Using the Object-Oriented Paradigm distills new tools to extract information from remotely sensed data.
Practical Gis Analysis
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Author : David L. Verbyla
language : en
Publisher: CRC Press
Release Date : 2002-04-18
Practical Gis Analysis written by David L. Verbyla and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-04-18 with Technology & Engineering categories.
The hard part of problem solving using GIS analysis is the selection of the proper tools. The only practical guide for solving geo-spatial problems independent of specific GIS software and hardware, Practical GIS Analysis will teach you how GIS tools work, and how you can use them to solve problems in both vector and grid GIS worlds. The book inclu
Satellite Image Classification Methods
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Author : Pasquale De Marco
language : en
Publisher: Pasquale De Marco
Release Date : 2025-03-09
Satellite Image Classification Methods written by Pasquale De Marco and has been published by Pasquale De Marco this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-09 with Technology & Engineering categories.
Embark on a journey into the realm of satellite image classification with this comprehensive guide, unlocking the secrets of extracting valuable information from vast amounts of Earth observation data. Delve into the fundamental principles and cutting-edge techniques of this transformative technology, empowering yourself to harness the wealth of knowledge hidden within satellite imagery. Discover the intricacies of spectral, textural, and contextual analysis, and unravel the complexities of machine learning algorithms, object-based image analysis, and hyperspectral image classification. Explore specialized domains such as radar and SAR image classification, gaining insights into their unique characteristics and applications. Witness the synergistic power of multi-sensor and multi-temporal data integration, unlocking enhanced classification accuracy and temporal analysis capabilities. Delve into the importance of accuracy assessment and validation, ensuring the reliability and trustworthiness of your classification results. With real-world case studies and illustrative examples, this book brings the concepts and techniques to life, showcasing the practical applications of satellite image classification in diverse domains. From land cover mapping and environmental monitoring to disaster response and urban planning, see how this technology drives informed decision-making and addresses real-world challenges. Whether you are a student seeking a comprehensive understanding of satellite image classification, a researcher delving into advanced techniques, or a practitioner seeking to harness the power of this technology, this book is your ultimate resource. Its interdisciplinary approach and comprehensive coverage make it an essential guide for anyone seeking to unlock the transformative potential of satellite image classification. Immerse yourself in the world of satellite image classification and uncover the hidden insights within Earth observation data. This book is your key to unlocking the transformative power of this technology, empowering you to make informed decisions and drive positive change. If you like this book, write a review!
Unsupervised Learning In Space And Time
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Author : Marius Leordeanu
language : en
Publisher: Springer Nature
Release Date : 2020-04-17
Unsupervised Learning In Space And Time written by Marius Leordeanu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-17 with Computers categories.
This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way. Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.