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.
Supervised And Unsupervised Learning For Data Science
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Author : Michael W. Berry
language : en
Publisher: Springer Nature
Release Date : 2019-09-04
Supervised And Unsupervised Learning For Data Science written by Michael W. Berry and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-04 with Technology & Engineering categories.
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.
Unsupervised Classification
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Author :
language : en
Publisher: Springer
Release Date : 2012-12-13
Unsupervised Classification written by and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-13 with categories.
Unsupervised Learning Algorithms
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Author : M. Emre Celebi
language : en
Publisher: Springer
Release Date : 2016-04-29
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 2016-04-29 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.
Unsupervised Classification Learning From Cross Modal Environmental Structure
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Author : University of Rochester. Dept. of Computer Science
language : en
Publisher:
Release Date : 1994
Unsupervised Classification Learning From Cross Modal Environmental Structure written by University of Rochester. Dept. of Computer Science and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Learning categories.
Abstract: "This dissertation addresses the problem of unsupervised learning for pattern classification or category learning. A model that is based on gross cortical anatomy and implements biologically plausible computations is developed and shown to have classification power approaching that of a supervised discriminant algorithm. The advantage of supervised learning is that the final error metric is available during training. Unfortunately, when modeling human category learning, or in constructing classifiers for autonomous robots, one must deal with not having an omniscient entity labeling all incoming sensory patterns. We show that we can substitute for the labels by making use of structure between the pattern distributions to different sensory modalities. For example the co-occurrence of a visual image of a cow with a 'moo' sound can be used to simultaneously develop appropriate visual features for distinguishing the cow image and appropriate auditory features for recognizing the moo. We model human category learning as a process of minimizing the disagreement between outputs of sensory modalities processing temporally coincident patterns. We relate this mathematically to the optimal goal of minimizing the number of misclassifications in each modality and apply the idea to derive an algorithm for piecewise linear classifiers in which each network uses the output of the other networks as a supervisory signal. Using the Peterson-Barney vowel dataset we show that the algorithm finds appropriate placement for the classification boundaries. The alogrithm is then demonstrated on the task of learning to recognize acoustic and visual speech from images of lips and their emanating sounds. Performance on these tasks is within 1-7% of the related supervised algorithm (LVQ2.1). Finally we compare the algorithm to Becker's IMAX algorithm and give suggestions as to how the algorithm may be implemented in the brain using physiological results concerning the relationship between two types of neural plasticity, LTP and LTD, observed in visual cortical cells. We also show how the algorithm can be used as an efficient method for dealing with learning from data with missing values."
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.
Semi Supervised And Unsupervised Machine Learning
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Author : Amparo Albalate
language : en
Publisher: John Wiley & Sons
Release Date : 2013-01-24
Semi Supervised And Unsupervised Machine Learning written by Amparo Albalate 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 2013-01-24 with Technology & Engineering categories.
This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described.
Machine Learning Foundations
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Author : Taeho Jo
language : en
Publisher: Springer Nature
Release Date : 2021-02-12
Machine Learning Foundations written by Taeho Jo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-12 with Technology & Engineering categories.
This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
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.
Kernel Based Algorithms For Mining Huge Data Sets
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Author : Te-Ming Huang
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-03-02
Kernel Based Algorithms For Mining Huge Data Sets written by Te-Ming Huang 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 2006-03-02 with Computers categories.
This is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction and shows the similarities and differences between the two most popular unsupervised techniques.