Unsupervised Machine Learning for Clustering in Political and Social Research, Philip D. Waggoner
Автор: Berry Michael W., Mohamed Azlinah, Yap Bee Wah Название: Supervised and Unsupervised Learning for Data Science ISBN: 3030224775 ISBN-13(EAN): 9783030224776 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Chapter1: A Systematic Review on Supervised & Unsupervised Machine Learning Algorithms for Data Science.- Chapter2: Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints.- Chapter3: Distributed Single-Source Shortest Path Algorithms with Two Dimensional Graph Layout.- Chapter4: Using Non-Negative Tensor Decomposition for Unsupervised Textual Influence Modeling.- Chapter5: Survival Support Vector Machines: A Simulation Study and Its Health-related Application.- Chapter6: Semantic Unsupervised Learning for Word Sense Disambiguation.- Chapter7: Enhanced Tweet Hybrid Recommender System using Unsupervised Topic Modeling and Matrix Factorization based Neural Network.- Chapter8: New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering.
Автор: Seetha Hari, Murty M. Narasimha, Tripathy B. K. Название: Modern Technologies for Big Data Classification and Clustering ISBN: 1522528059 ISBN-13(EAN): 9781522528050 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 31324.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage.Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics.Topics Covered:The many academic areas covered in this publication include, but are not limited to:Data visualizationDistributed Computing SystemsOpinion MiningPrivacy and securityRisk analysisSocial Network AnalysisText Data AnalyticsWeb Data Analytics
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems
Key Features
Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python
Master the art of data-driven problem-solving with hands-on examples
Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms
Book Description
Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.
The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You'll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you'll gain a thorough understanding of its theory and learn when to apply it. As you advance, you'll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.
By the end of this machine learning book, you'll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You'll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.
What you will learn
Understand when to use supervised, unsupervised, or reinforcement learning algorithms
Find out how to collect and prepare your data for machine learning tasks
Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff
Apply supervised and unsupervised algorithms to overcome various machine learning challenges
Employ best practices for tuning your algorithm's hyper parameters
Discover how to use neural networks for classification and regression
Build, evaluate, and deploy your machine learning solutions to production
Who this book is for
This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.
Автор: Yang Yun Название: Temporal Data Mining via Unsupervised Ensemble Learning ISBN: 0128116544 ISBN-13(EAN): 9780128116548 Издательство: Elsevier Science Рейтинг: Цена: 7241.00 р. Наличие на складе: Поставка под заказ.
Описание: Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. . Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. . Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics.
Автор: Li Xiangtao, Wong Ka-Chun Название: Natural Computing for Unsupervised Learning ISBN: 3319985655 ISBN-13(EAN): 9783319985657 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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 techniquesReports 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
Описание: This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition.
Автор: Celebi M. Emre, Aydin Kemal Название: Unsupervised Learning Algorithms ISBN: 3319795902 ISBN-13(EAN): 9783319795904 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Xiangtao Li; Ka-Chun Wong Название: Natural Computing for Unsupervised Learning ISBN: 3030075087 ISBN-13(EAN): 9783030075088 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Поставка под заказ.
Описание: 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 techniquesReports 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
Описание: Overview and Contributions.- Developments in Unsupervised Outlier Detection Research.- A Fast Distance-Based Outlier Detection Technique Using A Divisive Hierarchical Clustering Algorithm.- A k-Nearest Neighbour Centroid Based Outlier Detection Method.- A Minimum Spanning Tree Clustering Inspired Outlier Detection Technique.- A k-Nearest Neighbour Spectral Clustering Based Outlier Detection Technique.- Enhancing Outlier Detection by Filtering Out Core Points and Border Points.- An Effective Boundary Point Detection Algorithm via k-Nearest Neighbours Based Centroid.- A Nearest Neighbour Classifier Based Automated On-Line Novel Visual Percept Detection Method.- Unsupervised Fraud Detection in Environmental Time Series Data.
Описание: This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics.
Allows readers to analyze data sets with small samples and many features;Provides a fast algorithm, based upon linear algebra, to analyze big data;Includes several applications to multi-view data analyses, with a focus on bioinformatics.
Автор: Michael W. Berry; Azlinah Mohamed; Bee Wah Yap Название: Supervised and Unsupervised Learning for Data Science ISBN: 3030224740 ISBN-13(EAN): 9783030224745 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Fr?d?ric Ros; Serge Guillaume Название: Sampling Techniques for Supervised or Unsupervised Tasks ISBN: 3030293483 ISBN-13(EAN): 9783030293482 Издательство: Springer Рейтинг: Цена: 16070.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability and the “curse of dimensionality”, their implementations, evaluations, and applications. A large part of the book is dedicated to database comprising standard feature vectors, and a special section is reserved to the handling of more complex objects and dynamic scenarios. The book is ideal for anyone teaching or learning pattern recognition and interesting teaching or learning pattern recognition and is interested in the big data challenge. It provides an accessible introduction to the ?eld and discusses the state of the art concerning sampling techniques for supervised and unsupervised task.Provides a comprehensive description of sampling techniques for unsupervised and supervised tasks;Describe implementation and evaluation of algorithms that simultaneously manage scalable problems and curse of dimensionality;Addresses the role of sampling in dynamic scenarios, sampling when dealing with complex objects, and new challenges arising from big data. 'This book represents a timely collection of state-of-the art research of sampling techniques, suitable for anyone who wants to become more familiar with these helpful techniques for tackling the big data challenge.'M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer Science, University of Central Arkansas
'In science the difficulty is not to have ideas, but it is to make them work'From Carlo Rovelli
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