Описание: Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras.
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
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: Sanghamitra Bandyopadhyay; Sriparna Saha Название: Unsupervised Classification ISBN: 3642428363 ISBN-13(EAN): 9783642428364 Издательство: Springer Рейтинг: Цена: 6981.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a theoretical analysis of symmetry-based clustering techniques. It includes extensive real-world applications in data mining, remote sensing imaging, MR brain imaging, gene expression data analysis, and face detection.
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
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
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.
Описание: This book reports on an outstanding thesis thathas significantly advanced the state-of-the-art in the automated analysis andclassification of speech and music.
Автор: Basant Agarwal; Namita Mittal Название: Prominent Feature Extraction for Sentiment Analysis ISBN: 3319253417 ISBN-13(EAN): 9783319253411 Издательство: Springer Рейтинг: Цена: 18284.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
1 Introduction
2 Literature Survey
3 Machine Learning Approach for Sentiment Analysis
4 Semantic Parsing using Dependency Rules
5 Sentiment Analysis using ConceptNet Ontology and Context
Information
6 Semantic Orientation based Approach for Sentiment Analysis
7 Conclusions and FutureWork
References
Glossary Index
Автор: Li Hu; Zhiguo Zhang Название: EEG Signal Processing and Feature Extraction ISBN: 9811391122 ISBN-13(EAN): 9789811391125 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the conceptual and mathematical basis and the implementation of both electroencephalogram (EEG) and EEG signal processing in a comprehensive, simple, and easy-to-understand manner. EEG records the electrical activity generated by the firing of neurons within human brain at the scalp. They are widely used in clinical neuroscience, psychology, and neural engineering, and a series of EEG signal-processing techniques have been developed. Intended for cognitive neuroscientists, psychologists and other interested readers, the book discusses a range of current mainstream EEG signal-processing and feature-extraction techniques in depth, and includes chapters on the principles and implementation strategies.
Автор: Jyotismita Chaki; Nilanjan Dey Название: Texture Feature Extraction Techniques for Image Recognition ISBN: 9811508526 ISBN-13(EAN): 9789811508523 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book describes various texture feature extraction approaches and texture analysis applications. It introduces and discusses the importance of texture features, and describes various types of texture features like statistical, structural, signal-processed and model-based.
Автор: Urszula Sta?czyk; Beata Zielosko; Lakhmi C. Jain Название: Advances in Feature Selection for Data and Pattern Recognition ISBN: 3319675877 ISBN-13(EAN): 9783319675879 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents recent developments and research trends in the field of feature selection for data and pattern recognition, highlighting a number of recent advances. The field of feature selection is evolving constantly, providing numerous new algorithms, new solutions and new applications.
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