Download or read book Applications and Implementation of Kernel Principal Component Analysis to Specific Data Sets written by and published by . This book was released on 2011 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Kernel Based Algorithms for Mining Huge Data Sets written by Te-Ming Huang and published by Springer. This book was released on 2006-05-21 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Iterative Kernel Principal Component for Large Scale Data Set written by Weiya Shi and published by . This book was released on 2018 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel principal component analysis (KPCA) is a popular nonlinear feature extraction method that uses eigendecomposition techniques to extract the principal components in the feature space. Most of the existing approaches are not feasible for analyzing large-scale data sets because of extensive storage needs and computation costs. To overcome these disadvantages, an efficient iterative method for computing kernel principal components is proposed. First, the power iteration is used to compute the first eigenvalue and the corresponding eigenvector. Then Schur-Weilandt deflation is repeatedly applied to obtain other higher order eigenvectors. No computation and storage of the kernel matrix is involved in this procedure. Instead, each row of the kernel matrix is calculated sequentially through the iterations. Thus, the kernel principal components can be computed without relying on the traditional eigendecomposition. The space complexity of the proposed method isO(m), and the time complexity is also greatly reduced. We illustrate the effectiveness of our approach through a series of real data experiments.
Download or read book Support Vector Machines and Their Application in Chemistry and Biotechnology written by Yizeng Liang and published by CRC Press. This book was released on 2016-04-19 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support vector machines (SVMs) are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar wi
Download or read book Advances in Data Mining Theoretical Aspects and Applications written by Petra Perner and published by Springer. This book was released on 2007-08-18 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: The papers in this volume represent the proceedings of the 7th Industrial Conference on Data Mining. They are organized into topical sections on aspects of classification and prediction, clustering, web mining, data mining in medicine, applications of data mining, time series and frequent pattern mining, and association rule mining. Readers gain new insights into theories underlying data mining and discover state-of-the-technology applications.
Download or read book Proceedings of 3rd International Conference on Recent Trends in Machine Learning IoT Smart Cities and Applications written by Vinit Kumar Gunjan and published by Springer Nature. This book was released on 2023-02-23 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is a collection of best selected research papers presented at the International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications (ICMISC 2022) held during 28 – 29 March 2022 at CMR Institute of Technology, Hyderabad, Telangana, India. This book will contain the articles on current trends of machine learning, internet of things, and smart cities applications emphasizing on multi-disciplinary research in the area of artificial intelligence and cyber physical systems. The book is a great resource for scientists, research scholars and PG students to formulate their research ideas and find the future directions in these areas. Further, this book serves as a reference work to understand the latest technologies by practice engineers across the globe.
Download or read book Practical Machine Learning for Data Analysis Using Python written by Abdulhamit Subasi and published by Academic Press. This book was released on 2020-06-05 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data Explores important classification and regression algorithms as well as other machine learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features
Download or read book Advanced Concepts for Intelligent Vision Systems written by Jaques Blanc-Talon and published by Springer. This book was released on 2012-09-02 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 14th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2012, held in Brno, Czech Republic, in September 2012. The 46 revised full papers were carefully selected from 81 submissions and deal with image analysis and computer vision with a focus on detection, recognition, tracking and identification.
Download or read book Python Machine Learning written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2015-09-23 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Download or read book Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods written by Chris Aldrich and published by Springer Science & Business Media. This book was released on 2013-06-15 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.
Download or read book Principal Component Analysis written by Parinya Sanguansat and published by BoD – Books on Demand. This book was released on 2012-03-02 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction.
Download or read book Computational Intelligence in Decision and Control written by Da Ruan and published by World Scientific. This book was released on 2008 with total page 1201 pages. Available in PDF, EPUB and Kindle. Book excerpt: FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to Computational Intelligence for applied research. The contributions to the eighth edition in the series of FLINS conferences cover state-of-the-art research, development, and technology for computational intelligence systems in general, and for intelligent decision and control in particular.
Download or read book Proceedings of the 3rd International Conference on Digital Economy and Computer Application DECA 2023 written by Charles Chen and published by Springer Nature. This book was released on 2024-01-02 with total page 771 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access book. The 3rd International Conference on Digital Economy and Computer Applications (DECA 2023) will be held on September 22–24, 2023 in Shanghai, China. Digital economy is the main economic form after agricultural economy and industrial economy. It takes data resources as the key element, modern information network as the main carrier, and the integration and application of information and communication technology and all-factor digital transformation as the important driving force to promote a new economic form that is more unified in fairness and efficiency. The essence of digital economy is informationization. Informatization is a social and economic process caused by the revolution of production tools, such as computer and Internet, from industrial economy to information economy. The theme of the conference mainly focuses on digital economy and computer applications and other related research fields, aiming to provide an international cooperation and exchange platform for experts and scholars in related research fields and enterprise development personnel to share research results, discuss existing problems and challenges, and explore cutting-edge technologies. We sincerely invite experts and scholars from universities and research institutions at home and abroad, entrepreneurs and other relevant personnel to contribute and participate in the conference. The DECA 2023 is accepting papers for proceeding publication. We accept contributions from those who care about exploring and enhancing the research and innovation in Digital Economy and Computer Applications in the world. The directions of the call for papers are as follows: Internet of Things (IoT), Blockchain Technology, Service-Oriented and Cloud, Industry Track, Deliver the Intelligent Enterprise, Mobile business and Autonomous Computing and other papers in line with the direction of digital economy and computer applications. We welcome submissions from scholars, students, and practitioners across many disciplines that contribute to the study and practice of Digital Economy and Computer Applications.
Download or read book Machine Learning for Application Layer Intrusion Detection written by Konrad and published by Lulu.com. This book was released on 2011-09-21 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is concerned with the automatic detection of unknown attacks in network communication. Based on concepts of machine learning, a framework for self-learning intrusion detection is proposed which enables accurate and efficient identification of attacks in the application layer of network communication. The book is a doctoral thesis and targets researchers and postgraduate students in the area of computer security and machine learning.
Download or read book AI 2005 Advances in Artificial Intelligence written by Shichao Zhang and published by Springer Science & Business Media. This book was released on 2005-11-21 with total page 1369 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 18th Australian Joint Conference on Artificial Intelligence, AI 2005, held in Sydney, Australia in December 2005. The 77 revised full papers and 119 revised short papers presented together with the abstracts of 3 keynote speeches were carefully reviewed and selected from 535 submissions. The papers are catgorized in three broad sections, namely: AI foundations and technologies, computational intelligence, and AI in specialized domains. Particular topics addressed by the papers are logic and reasoning, machine learning, game theory, robotic technology, data mining, neural networks, fuzzy theory and algorithms, evolutionary computing, Web intelligence, decision making, pattern recognition, agent technology, and AI applications.
Download or read book Machine Learning Theory and Applications written by Xavier Vasques and published by John Wiley & Sons. This book was released on 2024-01-11 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs) Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.
Download or read book Optimizing Intelligent Systems for Cross Industry Application written by Rajest, S. Suman and published by IGI Global. This book was released on 2024-08-28 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent systems, powered by artificial intelligence (AI) and machine learning, offer transformative benefits across diverse sectors, from healthcare and finance to manufacturing and agriculture. By refining these systems to be more adaptable, scalable, and informative, industries can solve complex business problems and streamline operations. Effective research into technical challenges across intelligent system application is necessary to prioritize their development and impact in industries, such as crop analysis, disease diagnosis, or traffic management. Optimizing Intelligent Systems for Cross-Industry Application explores the challenges and opportunities associated with intelligent technology integration in various sectors, including agriculture, medicine, healthcare, computer engineering, business management, and environmental research. It presents solutions for the effective use of intelligent systems within their respective industries. This book covers topics such as human resources, smart cities, and crop productivity, and is a useful resource for computer engineers, agriculturalists, business owners, healthcare professionals, environmentalists, researchers, scientists, and academicians.
