Download or read book Unsupervised Classification written by Sanghamitra Bandyopadhyay and published by Springer Science & Business Media. This book was released on 2012-12-13 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Download or read book The Effect of Training Block Size on Unsupervised Classification of Landsat Thematic Mapper Imagery written by Paul W. Snook and published by . This book was released on 1991 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
Download or read book Hands On Unsupervised Learning Using Python written by Ankur A. Patel and published by "O'Reilly Media, Inc.". This book was released on 2019-02-21 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Unsupervised Learning written by Matthew Kyan and published by John Wiley & Sons. This book was released on 2014-05-02 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Unsupervised Learning in Space and Time written by Marius Leordeanu and published by Springer Nature. This book was released on 2020-04-17 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Download or read book The Unsupervised Learning Workshop written by Aaron Jones and published by Packt Publishing Ltd. This book was released on 2020-07-29 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginner's workshop, featuring interesting examples and activities Key FeaturesGet familiar with the ecosystem of unsupervised algorithmsLearn interesting methods to simplify large amounts of unorganized dataTackle real-world challenges, such as estimating the population density of a geographical areaBook Description Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner. The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding. As you progress, you'll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, you'll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area. By the end of this book, you'll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights. What you will learnDistinguish between hierarchical clustering and the k-means algorithmUnderstand the process of finding clusters in dataGrasp interesting techniques to reduce the size of dataUse autoencoders to decode dataExtract text from a large collection of documents using topic modelingCreate a bag-of-words model using the CountVectorizerWho this book is for If you are a data scientist who is just getting started and want to learn how to implement machine learning algorithms to build predictive models, then this book is for you. To expedite the learning process, a solid understanding of the Python programming language is recommended, as you'll be editing classes and functions instead of creating them from scratch.
Download or read book Satellite Image Analysis Clustering and Classification written by Surekha Borra and published by Springer. This book was released on 2019-02-08 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time. This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.
Download or read book Applied Unsupervised Learning with Python written by Benjamin Johnston and published by Packt Publishing Ltd. This book was released on 2019-05-28 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data Key FeaturesLearn how to select the most suitable Python library to solve your problemCompare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use themDelve into the applications of neural networks using real-world datasetsBook Description Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you on the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The course begins by explaining how basic clustering works to find similar data points in a set. Once you are well versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. You will complete the course by challenging yourself through various interesting activities such as performing a Market Basket Analysis and identifying relationships between different merchandises. By the end of this course, you will have the skills you need to confidently build your own models using Python. What you will learnUnderstand the basics and importance of clusteringBuild k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packagesExplore dimensionality reduction and its applicationsUse scikit-learn (sklearn) to implement and analyse principal component analysis (PCA)on the Iris datasetEmploy Keras to build autoencoder models for the CIFAR-10 datasetApply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction dataWho this book is for This course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial.
Download or read book Unsupervised Learning and Reverse Optical Flow in Mobile Robotics written by Andrew Lookingbill and published by Stanford University. This book was released on 2011 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: As sensor resolution increases and costs decrease, the amount of data available on mobile robotics platforms is exploding. Unsupervised machine learning algorithms, and their ability to produce useful information without large labeled training sets, are an important tool for benefiting from this abundance. In this thesis the application of unsupervised learning to three subfields of mobile robotics is discussed. Tracking multiple moving objects from an unmanned aerial vehicle, road following in loosely-structured environments, and autonomous offroad navigation. The thesis focuses on building dynamic activity-based ground models for multi-object tracking, the combination of optical flow techniques and dynamic programming to estimate the location of a road, and the use of optical flow techniques to improve the quality of an autonomous robot's obstacle classification.
Download or read book Natural Computing for Unsupervised Learning written by Xiangtao Li and published by Springer. This book was released on 2018-10-31 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learning. Includes advances on unsupervised learning using natural computing techniques Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning Features natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms
Download or read book Practical GIS Analysis written by David L. Verbyla and published by CRC Press. This book was released on 2002-04-18 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Contextual Image Classification written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2024-05-04 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is Contextual Image Classification A method of classification that is based on the contextual information contained in images is referred to as contextual image classification. This method falls under the category of pattern recognition in computer vision. A "contextual" approach is one that focuses on the relationship between the pixels that are in close proximity to one another, which is also referred to as the neighborhood. The classification of the photographs by the utilization of the contextual information is the objective of this approach. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Contextual image classification Chapter 2: Pattern recognition Chapter 3: Gaussian process Chapter 4: LPBoost Chapter 5: One-shot learning (computer vision) Chapter 6: Least-squares support vector machine Chapter 7: Fraunhofer diffraction equation Chapter 8: Symmetry in quantum mechanics Chapter 9: Bayesian hierarchical modeling Chapter 10: Paden-Kahan subproblems (II) Answering the public top questions about contextual image classification. (III) Real world examples for the usage of contextual image classification in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Contextual Image Classification.
Download or read book Advanced Lectures on Machine Learning written by Olivier Bousquet and published by Springer. This book was released on 2011-03-22 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.
Download or read book Remote Sensing Digital Image Analysis written by John A. Richards and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Revised and enlarged to reflect new developments in the field, the fourth edition of this well-established text provides an introduction to quantitative evaluation of satellite- and aircraft-derived remotely retrieved data. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations.
