Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)—classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twin SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised problems, and SVMs for problems with perturbations. To improve readability, concepts, methods, and results are introduced graphically and with clear explanations. For important concepts and algorithms, such as the Crammer-Singer SVM for multi-class classification problems, the text provides geometric interpretations that are not depicted in current literature. Enabling a sound understanding of SVMs, this book gives beginners as well as more experienced researchers and engineers the tools to solve real-world problems using SVMs.
This book is mainly about an innovative and fundamental method called “intelligent knowledge” to bridge the gap between data mining and knowledge management, two important fields recognized by the information technology (IT) community and business analytics (BA) community respectively. The book includes definitions of the “first-order” analytic process, “second-order” analytic process and intelligent knowledge, which have not formally been addressed by either data mining or knowledge management. Based on these concepts, which are especially important in connection with the current Big Data movement, the book describes a framework of domain-driven intelligent knowledge discovery. To illustrate its technical advantages for large-scale data, the book employs established approaches, such as Multiple Criteria Programming, Support Vector Machine and Decision Tree to identify intelligent knowledge incorporated with human knowledge. The book further shows its applicability by means of real-life data analyses in the contexts of internet business and traditional Chinese medicines.
The book covers up-to-date theoretical and applied advances in grey systems theory from across the world and vividly presents the reader with the overall picture of this new theory and its frontier research. Many of the concepts, models and methods in the book are original by the authors, including simplified form of grey number, general grey number and the operations of grey numbers; the axiomatic system of buffer operators and a series of weakening and strengthening operators; a series of grey relational analysis models, including grey absolute, relative, synthetic, similarity, closeness, negative and three dimension degree, etc.; grey fixed weight clustering model, grey evaluation models based on center-point and end-point mixed possibility functions; original difference grey model (ODGM), even difference grey model (EDGM), discrete grey model (DGM), fractional grey models, self-memory grey models; multi-attribute intelligent grey target decision models, weight vector group with kernel and the weighted comprehensive clustering coefficient vector, and spectrum analysis of sequence operators, etc. This book will be appropriate as a reference and/or professional book for courses of grey system theory for graduate students or high-level undergraduate students, majoring in areas of science, technology, agriculture, medicine, astronomy, earth science, economics, and management. It can also be utilized by researchers and practitioners in research institutions, business entities, and government agencies.
In the vast majority of literature on 'Chinese nationalism' the distinction between nation and state is rarely made, consequently nationalism usually appears as loyalty to the state rather than identification with the nation. Yet, since 1989, both the official configuration of the nation and the state's monopolized right to name the nation have come under rigorous challenge. Cultural Nationalism in Contemporary China relocates the discussion of nationalism to within a more contemporary framework which explores the disjunction between the people and the state and the relationship of each to the nation. With its challenging exploration of one of the most neglected aspects of identity in China, this book should appeal to Asianists, China watchers and all of those with an interest in cultural and sociological phenomena in East Asia.
Examining the interaction between the Communist Party of China (CCP) and specific social categories (including peasants, workers, the middle classes, and the dominant class), with a focus on class and class discourse, this volume analyses the CCP’s impact on social change in China between 1921 and 1978. By exploring the CCP’s evolving discourse of class, this book demonstrates that, while class has retained its centrality, its meaning has been re-articulated from an ideological-political tool to a less meaningful signifier, though always used instrumentality. By examining the impact of the CCP’s policies and discourse surrounding class, it also reveals how its own policies since 1921 have shaped the CCP’s current (2021) perspectives on class and stratification. This volume, through an analysis of economic, political, and cultural inequalities in Chinese society even after 1949, also reveals the emergence of a diverse and often overlooked middle class in Chinese society during the 1950s. Delivering a detailed analysis of how the CCP has developed its practical approaches to class and mobilization, this study will be of interest to students and scholars of Chinese politics, Chinese history, Asian politics, and Asian studies.
By examining the changing political economy in China through detailed studies of the peasantry, workers, middle classes, and the dominant class, this volume reveals the Communist Party of China’s (CCP’s) impact on social change in China between 1978 and 2021. This book explores in depth the CCP’s programme of reform and openness that had a dramatic impact on China’s socio-economic trajectory following the death of Mao Zedong and the end of the Cultural Revolution. It also goes on to chart the acceptance of Market Socialism, highlighting the resulting emergence of a larger middle class, while also appreciating the profound consequences this created for workers and peasants. Additionally, this volume examines the development of the dominant class which remains a defining feature of China’s political economy and the Party-state. Providing an in-depth analysis of class as understood by the CCP in conjunction with sociological interpretations of socio-economic and socio-political change, this study will be of interest to students and scholars of Chinese Politics, Chinese History, Asian Politics, and Asian studies.
Authoritative resource systematically introducing JCAS technologies and providing valuable information and knowledge to researchers and engineers Based on over six years of dedicated research to joint communications and sensing (JCAS) by the authors and their collaborators and students, Joint Communications and Sensing is the first book to comprehensively cover the subject of JCAS, which is expected to deliver huge cost and energy savings, and therefore has become a hallmark of future 6G and next generation radar technologies. The book has three parts. Part I presents the basic JCAS concepts and applications and the basic signal processing algorithms to support JCAS. Part II covers communications-centric JCAS designs that describe how sensing can be integrated into communications networks such as 5G and 6G. Part III presents ways to integrate communications in various radar sensing technologies and platforms. Specific sample topics covered in Joint Communications and Sensing include: Three categories of JCAS systems, potential sensing applications of JCAS, signal processing fundamentals, and channel models for communications and radar Frameworks for perceptive mobile networks (PMNs), system modifications to enable PMN sensing, and PMN system issues Orthogonal time-frequency space waveform-based JCAS for IoT, including signal models, echo pre-processing, and target parameter estimation Joint Communications and Sensing provides valuable information and knowledge to researchers and engineers in the communications and radar sensing communities and industries, enabling them to upskill and prepare for JCAS technology research and development. The text is of particular interest to engineers in the wireless communications industry who are pursuing new capabilities in 6G.
Advanced Antenna Array Engineering for 6G and Beyond Wireless Communications Reviews advances in the design and deployment of antenna arrays for future generations of wireless communication systems, offering new solutions for the telecommunications industry Advanced Antenna Array Engineering for 6G and Beyond Wireless Communications addresses the challenges in designing and deploying antennas and antenna arrays which deliver 6G and beyond performance with high energy efficiency and possess the capability of being immune to interference caused by different systems mounted on the same platforms. This timely and authoritative volume presents innovative solutions for developing integrated communications networks of high-gain, individually-scannable, multi-beam antennas that are reconfigurable and conformable to all platforms, thus enabling the evolving integrated land, air and space communications networks. The text begins with an up-to-date discussion of the engineering issues facing future wireless communications systems, followed by a detailed discussion of different beamforming networks for multi-beam antennas. Subsequent chapters address problems of 4G/5G antenna collocation, discuss differentially-fed antenna arrays, explore conformal transmit arrays for airborne platforms, and present latest results on fixed frequency beam scanning leaky wave antennas as well as various analogue beam synthesizing strategies. Based primarily on the authors’ extensive work in the field, including original research never before published, this important new volume: Reviews multi-beam feed networks, array decoupling and de-scattering methods Provides a systematic study on differentially fed antenna arrays that are resistant to interference caused by future multifunctional/multi-generation systems Features previously unpublished material on conformal transmit arrays based on Huygen’s metasufaces and reconfigurable leaky wave antennas Includes novel algorithms for synthesizing and optimizing thinned massive arrays, conformal arrays, frequency invariant arrays, and other future arrays Advanced Antenna Array Engineering for 6G and Beyond Wireless Communications is an invaluable resource for antenna engineers and researchers, as well as graduate and senior undergraduate students in the field.
This thesis focuses on the growth of a new type of two-dimensional (2D) material known as hexagonal boron nitride (h-BN) using chemical vapor deposition (CVD). It also presents several significant breakthroughs in the authors’ understanding of the growth mechanism and development of new growth techniques, which are now well known in the field. Of particular importance is the pioneering work showing experimental proof that 2D crystals of h-BN can indeed be hexagonal in shape. This came as a major surprise to many working in the 2D field, as it had been generally assumed that hexagonal-shaped h-BN was impossible due to energy dynamics. Beyond growth, the thesis also reports on synthesis techniques that are geared toward commercial applications. Large-area aligned growth and up to an eightfold reduction in the cost of h-BN production are demonstrated. At present, all other 2D materials generally use h-BN as their dielectric layer and for encapsulation. As such, this thesis lays the cornerstone for using CVD 2D h-BN for this purpose.
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)—classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twin SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised problems, and SVMs for problems with perturbations. To improve readability, concepts, methods, and results are introduced graphically and with clear explanations. For important concepts and algorithms, such as the Crammer-Singer SVM for multi-class classification problems, the text provides geometric interpretations that are not depicted in current literature. Enabling a sound understanding of SVMs, this book gives beginners as well as more experienced researchers and engineers the tools to solve real-world problems using SVMs.
This book is mainly about an innovative and fundamental method called “intelligent knowledge” to bridge the gap between data mining and knowledge management, two important fields recognized by the information technology (IT) community and business analytics (BA) community respectively. The book includes definitions of the “first-order” analytic process, “second-order” analytic process and intelligent knowledge, which have not formally been addressed by either data mining or knowledge management. Based on these concepts, which are especially important in connection with the current Big Data movement, the book describes a framework of domain-driven intelligent knowledge discovery. To illustrate its technical advantages for large-scale data, the book employs established approaches, such as Multiple Criteria Programming, Support Vector Machine and Decision Tree to identify intelligent knowledge incorporated with human knowledge. The book further shows its applicability by means of real-life data analyses in the contexts of internet business and traditional Chinese medicines.
Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.
This will help us customize your experience to showcase the most relevant content to your age group
Please select from below
Login
Not registered?
Sign up
Already registered?
Success – Your message will goes here
We'd love to hear from you!
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.