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Data Analytics On Graphs Introduction 2 Geometrically Defined Graph Topologies 3 Graph Topology Based On Signal Similarity 4 Learning Of Graph Laplacian From Data 5 From Newton Minimization To Graphical Lasso Via Lasso 6 Physically Well Defined Graphs 7 Graph Learning From Data And External Sources 8 Random Signal Simulation On Graphs 9 Summary Of Graph Learning From Data Using Probabilistic Generative Models 10 Graph Neural Networks 11 Tensor Representation Of Lattice Structured Graphs 12 Metro Traffic Modeling Through Graphs 13 Portfolio Cuts 14 Conclusion Acknowledgments References
Download Data Analytics On Graphs Introduction 2 Geometrically Defined Graph Topologies 3 Graph Topology Based On Signal Similarity 4 Learning Of Graph Laplacian From Data 5 From Newton Minimization To Graphical Lasso Via Lasso 6 Physically Well Defined Graphs 7 Graph Learning From Data And External Sources 8 Random Signal Simulation On Graphs 9 Summary Of Graph Learning From Data Using Probabilistic Generative Models 10 Graph Neural Networks 11 Tensor Representation Of Lattice Structured Graphs 12 Metro Traffic Modeling Through Graphs 13 Portfolio Cuts 14 Conclusion Acknowledgments References full books in PDF, epub, and Kindle. Read online Data Analytics On Graphs Introduction 2 Geometrically Defined Graph Topologies 3 Graph Topology Based On Signal Similarity 4 Learning Of Graph Laplacian From Data 5 From Newton Minimization To Graphical Lasso Via Lasso 6 Physically Well Defined Graphs 7 Graph Learning From Data And External Sources 8 Random Signal Simulation On Graphs 9 Summary Of Graph Learning From Data Using Probabilistic Generative Models 10 Graph Neural Networks 11 Tensor Representation Of Lattice Structured Graphs 12 Metro Traffic Modeling Through Graphs 13 Portfolio Cuts 14 Conclusion Acknowledgments References ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Data Analytics on Graphs by : Ljubisa Stankovic
Download or read book Data Analytics on Graphs written by Ljubisa Stankovic and published by . This book was released on 2020-12-22 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aimed at readers with a good grasp of the fundamentals of data analytics, this book sets out the fundamentals of graph theory and the emerging mathematical techniques for the analysis of a wide range of data acquired on graph environments. This book will be a useful friend and a helpful companion to all involved in data gathering and analysis.
Book Synopsis Distributed Optimization for Smart Cyber-Physical Networks by : Giuseppe Notarstefano
Download or read book Distributed Optimization for Smart Cyber-Physical Networks written by Giuseppe Notarstefano and published by . This book was released on 2019-12-11 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an increasingly connected world, the term cyber-physical networks has been coined to refer to the communication among devices that is turning smart devices into smart (cooperating) systems. The distinctive feature of such systems is that significant advantage can be obtained if its interconnected, complex nature is exploited. Several challenges arising in cyber-physical networks can be stated as optimization problems. Examples are estimation, decision, learning and control applications. In cyber-physical networks, the goal is to design algorithms, based on the exchange of information among the processors, that take advantage of the aggregated computational power. Distributed Optimization for Smart Cyber-Physical Networks provides a comprehensive overview of the most common approaches used to design distributed optimization algorithms, together with the theoretical analysis of the main schemes in their basic version. It identifies and formalizes classes of problem set-ups that arise in motivating application scenarios. For each set-up, in order to give the main tools for analysis, tailored distributed algorithms in simplified cases are reviewed. Extensions and generalizations of the basic schemes are also discussed at the end of each chapter. Distributed Optimization for Smart Cyber-Physical Networks provides the reader with an accessible overview of the current research and gives important pointers towards new developments. It is an excellent starting point for research and students unfamiliar with the topic.
Book Synopsis Vertex-Frequency Analysis of Graph Signals by : Ljubiša Stanković
Download or read book Vertex-Frequency Analysis of Graph Signals written by Ljubiša Stanković and published by Springer. This book was released on 2018-12-01 with total page 507 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces new methods to analyze vertex-varying graph signals. In many real-world scenarios, the data sensing domain is not a regular grid, but a more complex network that consists of sensing points (vertices) and edges (relating the sensing points). Furthermore, sensing geometry or signal properties define the relation among sensed signal points. Even for the data sensed in the well-defined time or space domain, the introduction of new relationships among the sensing points may produce new insights in the analysis and result in more advanced data processing techniques. The data domain, in these cases and discussed in this book, is defined by a graph. Graphs exploit the fundamental relations among the data points. Processing of signals whose sensing domains are defined by graphs resulted in graph data processing as an emerging field in signal processing. Although signal processing techniques for the analysis of time-varying signals are well established, the corresponding graph signal processing equivalent approaches are still in their infancy. This book presents novel approaches to analyze vertex-varying graph signals. The vertex-frequency analysis methods use the Laplacian or adjacency matrix to establish connections between vertex and spectral (frequency) domain in order to analyze local signal behavior where edge connections are used for graph signal localization. The book applies combined concepts from time-frequency and wavelet analyses of classical signal processing to the analysis of graph signals. Covering analytical tools for vertex-varying applications, this book is of interest to researchers and practitioners in engineering, science, neuroscience, genome processing, just to name a few. It is also a valuable resource for postgraduate students and researchers looking to expand their knowledge of the vertex-frequency analysis theory and its applications. The book consists of 15 chapters contributed by 41 leading researches in the field.
Download or read book The Lion Way written by Roberto Battiti and published by Createspace Independent Publishing Platform. This book was released on 2014-02-21 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex and dynamic problems. The LION way is about increasing the automation level and connecting data directly to decisions and actions. More power is directly in the hands of decision makers in a self-service manner, without resorting to intermediate layers of data scientists. LION is a complex array of mechanisms, like the engine in an automobile, but the user (driver) does not need to know the inner workings of the engine in order to realize its tremendous benefits. LION's adoption will create a prairie fire of innovation which will reach most businesses in the next decades. Businesses, like plants in wildfire-prone ecosystems, will survive and prosper by adapting and embracing LION techniques, or they risk being transformed from giant trees to ashes by the spreading competition.
Book Synopsis Neural Networks and Statistical Learning by : Ke-Lin Du
Download or read book Neural Networks and Statistical Learning written by Ke-Lin Du and published by Springer Science & Business Media. This book was released on 2013-12-09 with total page 834 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.
Book Synopsis Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002 by : Takeyoshi Dohi
Download or read book Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002 written by Takeyoshi Dohi and published by Springer Science & Business Media. This book was released on 2002-09-13 with total page 722 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fifth international Conference in Medical Image Computing and Computer Assisted Intervention (MICCAI 2002) was held in Tokyo from September 25th to 28th, 2002. This was the first time that the conference was held in Asia since its foundation in 1998. The objective of the conference is to offer clinicians and scientists the opportunity to collaboratively create and explore the new medical field. Specifically, MICCAI offers a forum for the discussion of the state of art in computer-assisted interentions, medical robotics, and image processing among experts from multi-disciplinary professions, including but not limited to clinical doctors, computer scientists, and mechanical and biomedical engineers. The expectations of society are very high; the advancement of medicine will depend on computer and device technology in coming decades, as they did in the last decades. We received 321 manuscripts, of which 41 were chosen for oral presentation and 143 for poster presentation. Each paper has been included in these proceedings in eight-page full paper format, without any differentiation between oral and poster papers. Adherence to this full paper format, along with the increased number of manuscripts, surpassing all our expectations, has led us to issue two proceedings volumes for the first time in MICCAI’s history. Keeping to a single volume by assigning fewer pages to each paper was certainly an option for us considering our budget constraints. However, we decided to increase the volume to offer authors maximum opportunity to argue the state of art in their work and to initiate constructive discussions among the MICCAI audience.
Book Synopsis Statistical Analysis of Network Data with R by : Eric D. Kolaczyk
Download or read book Statistical Analysis of Network Data with R written by Eric D. Kolaczyk and published by Springer. This book was released on 2014-05-22 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).
Book Synopsis High-Dimensional Probability by : Roman Vershynin
Download or read book High-Dimensional Probability written by Roman Vershynin and published by Cambridge University Press. This book was released on 2018-09-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
Book Synopsis Linear Algebra and Optimization for Machine Learning by : Charu C. Aggarwal
Download or read book Linear Algebra and Optimization for Machine Learning written by Charu C. Aggarwal and published by Springer Nature. This book was released on 2020-05-13 with total page 507 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.
Book Synopsis Adaptation in Natural and Artificial Systems by : John H. Holland
Download or read book Adaptation in Natural and Artificial Systems written by John H. Holland and published by MIT Press. This book was released on 1992-04-29 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications. In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics. Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements.
Book Synopsis Big Data Preprocessing by : Julián Luengo
Download or read book Big Data Preprocessing written by Julián Luengo and published by Springer Nature. This book was released on 2020-03-16 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensible overview of Big Data Preprocessing, which includes a formal description of each problem. It also focuses on the most relevant proposed solutions. This book illustrates actual implementations of algorithms that helps the reader deal with these problems. This book stresses the gap that exists between big, raw data and the requirements of quality data that businesses are demanding. This is called Smart Data, and to achieve Smart Data the preprocessing is a key step, where the imperfections, integration tasks and other processes are carried out to eliminate superfluous information. The authors present the concept of Smart Data through data preprocessing in Big Data scenarios and connect it with the emerging paradigms of IoT and edge computing, where the end points generate Smart Data without completely relying on the cloud. Finally, this book provides some novel areas of study that are gathering a deeper attention on the Big Data preprocessing. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing techniques applied and some other open problems. Practitioners and data scientists who work in this field, and want to introduce themselves to preprocessing in large data volume scenarios will want to purchase this book. Researchers that work in this field, who want to know which algorithms are currently implemented to help their investigations, may also be interested in this book.
Book Synopsis 5th International Conference on Biomedical Engineering in Vietnam by : Vo Van Toi
Download or read book 5th International Conference on Biomedical Engineering in Vietnam written by Vo Van Toi and published by Springer. This book was released on 2014-11-18 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the proceedings of the Fifth International Conference on the Development of Biomedical Engineering in Vietnam which was held from June 16-18, 2014 in Ho Chi Minh City. The volume reflects the progress of Biomedical Engineering and discusses problems and solutions. I aims identifying new challenges, and shaping future directions for research in biomedical engineering fields including medical instrumentation, bioinformatics, biomechanics, medical imaging, drug delivery therapy, regenerative medicine and entrepreneurship in medical devices.
Book Synopsis Machine Learning Control – Taming Nonlinear Dynamics and Turbulence by : Thomas Duriez
Download or read book Machine Learning Control – Taming Nonlinear Dynamics and Turbulence written by Thomas Duriez and published by Springer. This book was released on 2016-11-02 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube.
Book Synopsis Foundations of Signal Processing by : Martin Vetterli
Download or read book Foundations of Signal Processing written by Martin Vetterli and published by Cambridge University Press. This book was released on 2014-09-04 with total page 745 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive and engaging textbook introduces the basic principles and techniques of signal processing, from the fundamental ideas of signals and systems theory to real-world applications. Students are introduced to the powerful foundations of modern signal processing, including the basic geometry of Hilbert space, the mathematics of Fourier transforms, and essentials of sampling, interpolation, approximation and compression The authors discuss real-world issues and hurdles to using these tools, and ways of adapting them to overcome problems of finiteness and localization, the limitations of uncertainty, and computational costs. It includes over 160 homework problems and over 220 worked examples, specifically designed to test and expand students' understanding of the fundamentals of signal processing, and is accompanied by extensive online materials designed to aid learning, including Mathematica® resources and interactive demonstrations.
Book Synopsis Computing and Network Sustainability by : Sheng-Lung Peng
Download or read book Computing and Network Sustainability written by Sheng-Lung Peng and published by Springer. This book was released on 2019-05-02 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a compilation of technical papers presented at the International Research Symposium on Computing and Network Sustainability (IRSCNS 2018) held in Goa, India on 30–31st August 2018. It covers areas such as sustainable computing and security, sustainable systems and technologies, sustainable methodologies and applications, sustainable networks applications and solutions, user-centered services and systems and mobile data management. Presenting novel and recent technologies, it is a valuable resource for researchers and industry professionals alike.
Book Synopsis Functional Properties of Advanced Engineering Materials and Biomolecules by : Felipe A. La Porta
Download or read book Functional Properties of Advanced Engineering Materials and Biomolecules written by Felipe A. La Porta and published by Springer Nature. This book was released on 2021-05-17 with total page 778 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how a small toolbox of experimental techniques, physical chemistry concepts as well as quantum/classical mechanics and statistical methods can be used to understand, explain and even predict extraordinary applications of these advanced engineering materials and biomolecules. It highlights how improving the material foresight by design, including the fundamental understanding of their physical and chemical properties, can provide new technological levels in the future.
Book Synopsis Generalized Curvatures by : Jean-Marie Morvan
Download or read book Generalized Curvatures written by Jean-Marie Morvan and published by Springer Science & Business Media. This book was released on 2008-05-13 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: The central object of this book is the measure of geometric quantities describing N a subset of the Euclidean space (E ,), endowed with its standard scalar product. Let us state precisely what we mean by a geometric quantity. Consider a subset N S of points of the N-dimensional Euclidean space E , endowed with its standard N scalar product. LetG be the group of rigid motions of E . We say that a 0 quantity Q(S) associated toS is geometric with respect toG if the corresponding 0 quantity Q[g(S)] associated to g(S) equals Q(S), for all g?G . For instance, the 0 diameter ofS and the area of the convex hull ofS are quantities geometric with respect toG . But the distance from the origin O to the closest point ofS is not, 0 since it is not invariant under translations ofS. It is important to point out that the property of being geometric depends on the chosen group. For instance, ifG is the 1 N group of projective transformations of E , then the property ofS being a circle is geometric forG but not forG , while the property of being a conic or a straight 0 1 line is geometric for bothG andG . This point of view may be generalized to any 0 1 subsetS of any vector space E endowed with a groupG acting on it.
