Download or read book Data Variant Kernel Analysis written by Yuichi Motai and published by John Wiley & Sons. This book was released on 2015-04-27 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state. Data-Variant Kernel Analysis: Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA) Develops group kernel analysis with the distributed databases to compare speed and memory usages Explores the possibility of real-time processes by synthesizing offline and online databases Applies the assembled databases to compare cloud computing environments Examines the prediction of longitudinal data with time-sequential configurations Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.

Download or read book OpenMP Advanced Task Based Device and Compiler Programming written by Simon McIntosh-Smith and published by Springer Nature. This book was released on 2023-08-30 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 19th International Workshop on OpenMP, IWOMP 2023, held in Bristol, UK, during September 13–15, 2023. The 15 full papers presented in this book were carefully reviewed and selected from 20 submissions. The papers are divided into the following topical sections: OpenMP and AI; Tasking Extensions; OpenMP Offload Experiences; Beyond Explicit GPU Support; and OpenMP Infrastructure and Evaluation.

Download or read book Artificial Intelligence for Healthy Longevity written by Alexey Moskalev and published by Springer Nature. This book was released on 2023-07-07 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state-of-the-art efforts to apply machine learning and AI methods for healthy aging and longevity research, diagnosis, and therapy development. The book examines the methods of machine learning and their application in the analysis of big medical data, medical images, the creation of algorithms for assessing biological age, and effectiveness of geroprotective medications. The promises and challenges of using AI to help achieve healthy longevity for the population are manifold. This volume, written by world-leading experts working at the intersection of AI and aging, provides a unique synergy of these two highly prominent fields and aims to create a balanced and comprehensive overview of the application methodology that can help achieve healthy longevity for the population. The book is accessible and valuable for specialists in AI and longevity research, as well as a wide readership, including gerontologists, geriatricians, medical specialists, and students from diverse fields, basic scientists, public and private research entities, and policy makers interested in potential intervention in degenerative aging processes using advanced computational tools.

Download or read book Fundamentals of Cognitive Radio written by Peyman Setoodeh and published by John Wiley & Sons. This book was released on 2017-07-31 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive treatment of cognitive radio networks and the specialized techniques used to improve wireless communications The human brain, as exemplified by cognitive radar, cognitive radio, and cognitive computing, inspires the field of Cognitive Dynamic Systems. In particular, cognitive radio is growing at an exponential rate. Fundamentals of Cognitive Radio details different aspects of the human brain and provides examples of how it can be mimicked by cognitive dynamic systems. The text offers a communication-theoretic background, including information on resource allocation in wireless networks and the concept of robustness. The authors provide a thorough mathematical background with data on game theory, variational inequalities, and projected dynamic systems. They then delve more deeply into resource allocation in cognitive radio networks. The text investigates the dynamics of cognitive radio networks from the perspectives of information theory, optimization, and control theory. It also provides a vision for the new world of wireless communications by integration of cellular and cognitive radio networks. This groundbreaking book: Shows how wireless communication systems increasingly use cognition to enhance their networks Explores how cognitive radio networks can be viewed as spectrum supply chain networks Derives analytic models for two complementary regimes for spectrum sharing (open-access and market-driven) to study both equilibrium and disequilibrium behaviors of networks Studies cognitive heterogeneous networks with emphasis on economic provisioning for resource sharing Introduces a framework that addresses the issue of spectrum sharing across licensed and unlicensed bands aimed for Pareto optimality Written for students of cognition, communication engineers, telecommunications professionals, and others, Fundamentals of Cognitive Radio offers a new generation of ideas and provides a fresh way of thinking about cognitive techniques in order to improve radio networks.

Download or read book Kernel Methods for Pattern Analysis written by John Shawe-Taylor and published by Cambridge University Press. This book was released on 2004-06-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.

Download or read book Multivariate Time Series Clustering Using Kernel Variant Multi way Principal Component Analysis written by Hwanseok Choi and published by . This book was released on 2010 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clustering multivariate time series data has been a challenging task for researchers since data has multiple dimensions to consider such as auto-correlations and cross-correlations whereas multivariate time series data has been prevailing in diverse areas for decades. However, for a short-period time series data, conventional time series modeling may not satisfy the model validity. Multi-way Principal Component Analysis can be used for this case, but the normality assumption can restrict to handle nonlinear data such as multivariate time series with high order interactions. Kernel variant MPCA will be proposed for an alternative solution for this case. To test if KMPCA can cluster trivariate time series data into two groups, two simulation studies were conducted. The first study has the same mean structure groups with error structures which are combinations of three different auto-correlation levels and three different cross-correlation levels. Two different mean structure groups with nine error structures were generated for the second study. To check the proposed method work well on a real-world data, Obesity-depression relationship study was done for a real-world data. The simulation studies showed that KMPCA cluster two different mean structure groups over 90% success rates when an appropriate kernel function with proper parameter was applied. Similar error structure will obstruct the clustering performance: strong cross-correlation, weak auto-correlation, and larger number of temporal points. Considering racial effect, obesity and obesity related variables, especially addictive material uses for 15 years can expect depressed cohorts at year 20 up to 76% for Caucasian group and 95% for African-American group.

Download or read book Visual Data Exploration and Analysis written by and published by . This book was released on 1995 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

Download or read book Optimization Issues in Data Analysis written by Alexander M. Malyscheff and published by . This book was released on 2001 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Kernel Methods in the Analysis of Big and Complex Data written by Hao Zhou and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel methods have achieved a lot of success in statistics and machine learning where linear models are often not sufficient to capture the relations and patterns in the data. For example, in smoothing spline models, a kernel is used to define a non-parametric class of the function so that one can fit a smoothing curve or surface to understand the relation between the response and predictors. Similarly, in Gaussian process, a kernel is used to define the covariance matrix so that one can understand the nonlinear trend of the data in a Bayesian manner. In support vector machine, a kernel is used to define a non-linear map to transform the predictors into a high-dimensional space so that one can find a hyperplane in the new space to distinguish data from different classes. All these methods over the past two decades have been successful in applications for biomedical science, finance, pattern recognition, and recommender systems. In modern data analysis, we occasionally face a number of interesting chal- lenges: our data may come from multiple heterogeneous sources, our data may be spatiotemporal, have few samples but lie in high dimensions, or our data may have a huge number of samples and require a method to understand the complex model. These challenges require us to consider new developments in statistics and kernel methods. In this work, we show how kernel methods can help us solve these challenges with other topics including domain adaptation, regularized statistics, deep learning, and deep Gaussian processes. In Chapter 2, we study the problem of analyzing data from multiple heteroge- neous sources. We derive a framework with a graphical causal model and maximum mean discrepancy to eliminate the biases between different datasets and to com- bine multiple datasets together in a systematic way for increased sample size and for improved statistical power. We use the framework for a problem motivated by Alzheimer's Disease research and show that we can successfully combine two datasets from different research centers to derive more accurate and consistent results. In Chapter 3, we continue our study on the problem of the analysis of multiple heterogeneous datasets but with a different focus. We derive new hypothesis tests and theoretical analysis to understand when it is beneficial to combine multiple datasets together, both in the low dimension setting and high dimension setting. We find that the problem is a bias-variance trade-off. When the reduction on the variance, which may due to increased sample size, is more than the increase on the bias from heterogeneous datasets, it is beneficial to combine different datasets even though they come from different sources. In Chapter 4, we study how to build a nonparametric model for spatiotemporal data when the samples are few but the dimension is high. We apply the frame- work on a Chicago crime dataset to understand the occurrence of crimes and it's transmission between various Chicago communities as time goes. The multiple communities and nonparametric kernel class make our model lie in high dimension but the limited crime events make our sample size small. We solve the problem by adding regularizations for the kernel-based models and we build a solid theo- retical foundation to understand the behavior of our models for high-dimensional spatiotemporal data. Finally, in Chapter 5, we propose a framework to understand the flow of the information in deep probabilistic models, which includes Bayesian neural networks, deep Gaussian processes, deep kernel learning, and others. On the one hand, we show that we can understand the information flow in deep probabilistic models using kernels and statistical structure assumptions and we show the relation be- tween Bayesian neural networks and deep Gaussian processes through theoretical analysis. On the other hand, our framework points out a way to extend additive models, hierarchical models and other statistical models with structure assump- tions to the deep structure so that we can use modern high computation power like deep learning while maintaining the good properties of statistical models including interpretability and uncertainty estimate.

Download or read book Machine Learning ECML written by and published by . This book was released on 2004 with total page 614 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Kernel Methods in Context dependent Fusion written by Gyeongyong Heo and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: By adding the regularization terms, not only does CDF-R achieve noise robustness, the main purpose of regularization, but the consequent clusters, which need not be convex, result in better performance than CDF. Although CDF-R is better at classification than CDF, the linear separability does not change. To completely remove the limitation, CDF is transformed to be non-linear, termed kernel-based context-dependent fusion (K-CDF). K-CDF adopts modified kernel methods to remove the restrictions of CDF and remedies some problems in the original kernel methods. K-CDF consists of three main components: dimension reduction, feature space clustering, and fusion. For each component, robust kernel fuzzy principal component analysis (RKF-PCA), kernel-based global fuzzy c-means (KG-FCM), and fuzzy support vector machine for noisy data (FSVM-N) are formulated, and which correspond to the robust variant of kernel PCA, kernel FCM, and fuzzy SVM, respectively. Although the three modifications were originated to address different shortcomings, one common purpose is to reduce the effect of nose, i.e., making the kernel methods noise-robust. By combining the three robust kernel methods, not only does K-CDF overcome the convex cluster assumption and linearly separable restriction, but it achieves noise robustness and better performance than previous methods.

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 Density Ratio Estimation in Machine Learning written by Masashi Sugiyama and published by Cambridge University Press. This book was released on 2012-02-20 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community.

Download or read book Intelligent Multimedia Computing Science written by Cyrus F. Nourani and published by . This book was released on 2005 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Multimedia Computing Science is an interdisciplinary field combining the arts, sciences, artificial intelligence, computer science, mathematics, and the humanities. The field presented is deeply rooted in Al, mathematical logic and models, modern communications, computer, and human sciences. Academic digital media studies are at times a partnership among Arts and Sciences, Computer Science, and Mathematics. The new fields encompass the intelligent and cognitive aspects of media arts and sciences, exploring the technical, cognitive, and aesthetic bases to human multimedia intelligence and its computation, the applications to business intelligence, model discovery, data mines and intelligent data bases, and IT. The monograph is a technical and practical book to the popular audience, to the business minded professionals, and to all groups wanting to be on an intelligent bearing to the new field.

Download or read book Gene Environment Interaction Analysis written by Sumiko Anno and published by CRC Press. This book was released on 2016-03-30 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene-environment (GE) interaction analysis is a statistical method for clarifying GE interactions applicable to a phenotype or a disease that is the result of interactions between genes and the environment. This book is the first to deal with the theme of GE interaction analysis. It compiles and details cutting-edge research in bioinformatics

Download or read book Theoretical Foundations of Functional Data Analysis with an Introduction to Linear Operators written by Tailen Hsing and published by John Wiley & Sons. This book was released on 2015-05-06 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators provides a uniquely broad compendium of the key mathematical concepts and results that are relevant for the theoretical development of functional data analysis (FDA). The self–contained treatment of selected topics of functional analysis and operator theory includes reproducing kernel Hilbert spaces, singular value decomposition of compact operators on Hilbert spaces and perturbation theory for both self–adjoint and non self–adjoint operators. The probabilistic foundation for FDA is described from the perspective of random elements in Hilbert spaces as well as from the viewpoint of continuous time stochastic processes. Nonparametric estimation approaches including kernel and regularized smoothing are also introduced. These tools are then used to investigate the properties of estimators for the mean element, covariance operators, principal components, regression function and canonical correlations. A general treatment of canonical correlations in Hilbert spaces naturally leads to FDA formulations of factor analysis, regression, MANOVA and discriminant analysis. This book will provide a valuable reference for statisticians and other researchers interested in developing or understanding the mathematical aspects of FDA. It is also suitable for a graduate level special topics course.