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 203 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 Learning Kernel Classifiers written by Ralf Herbrich and published by MIT Press. This book was released on 2022-11-01 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.

Download or read book Kernel Methods for Pattern Analysis written by and published by . This book was released on 2004 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: The kernel functions methodology described here provides a powerful and unified framework for disciplines ranging from neural networks and pattern recognition to machine learning and data mining. This book provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, suitable for standard pattern discovery problems.

Download or read book Analysis of Large and Complex Data written by Adalbert F.X. Wilhelm and published by Springer. This book was released on 2016-08-03 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a snapshot of the state-of-the-art in classification at the interface between statistics, computer science and application fields. The contributions span a broad spectrum, from theoretical developments to practical applications; they all share a strong computational component. The topics addressed are from the following fields: Statistics and Data Analysis; Machine Learning and Knowledge Discovery; Data Analysis in Marketing; Data Analysis in Finance and Economics; Data Analysis in Medicine and the Life Sciences; Data Analysis in the Social, Behavioural, and Health Care Sciences; Data Analysis in Interdisciplinary Domains; Classification and Subject Indexing in Library and Information Science. The book presents selected papers from the Second European Conference on Data Analysis, held at Jacobs University Bremen in July 2014. This conference unites diverse researchers in the pursuit of a common topic, creating truly unique synergies in the process.

Download or read book Kernel Methods and Machine Learning written by S. Y. Kung and published by Cambridge University Press. This book was released on 2014-04-17 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.

Download or read book Kernel Methods and Machine Learning written by S. Y. Kung and published by Cambridge University Press. This book was released on 2014-04-17 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.

Download or read book Kernel Methods in Bioengineering Signal and Image Processing written by Gustavo Camps-Valls and published by IGI Global. This book was released on 2007-01-01 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents an extensive introduction to the field of kernel methods and real world applications. The book is organized in four parts: the first is an introductory chapter providing a framework of kernel methods; the others address Bioegineering, Signal Processing and Communications and Image Processing"--Provided by publisher.

Download or read book Inference and Learning from Data Volume 2 written by Ali H. Sayed and published by Cambridge University Press. This book was released on 2022-12-22 with total page 1166 pages. Available in PDF, EPUB and Kindle. Book excerpt: This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.

Download or read book Inference and Learning from Data written by Ali H. Sayed and published by Cambridge University Press. This book was released on 2022-11-30 with total page 1081 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover data-driven learning methods with the third volume of this extraordinary three-volume set.

Download or read book Kernels For Structured Data written by Thomas Gartner and published by World Scientific. This book was released on 2008-08-29 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.

Download or read book Inference and Learning from Data Volume 1 written by Ali H. Sayed and published by Cambridge University Press. This book was released on 2022-12-22 with total page 1106 pages. Available in PDF, EPUB and Kindle. Book excerpt: This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.

Download or read book Data Science in Theory and Practice written by Maria Cristina Mariani and published by John Wiley & Sons. This book was released on 2021-10-12 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: DATA SCIENCE IN THEORY AND PRACTICE EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.

Download or read book Kernel Methods in Computer Vision written by Christoph H. Lampert and published by Now Publishers Inc. This book was released on 2009 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: Few developments have influenced the field of computer vision in the last decade more than the introduction of statistical machine learning techniques. Particularly kernel-based classifiers, such as the support vector machine, have become indispensable tools, providing a unified framework for solving a wide range of image-related prediction tasks, including face recognition, object detection and action classification. By emphasizing the geometric intuition that all kernel methods rely on, Kernel Methods in Computer Vision provides an introduction to kernel-based machine learning techniques accessible to a wide audience including students, researchers and practitioners alike, without sacrificing mathematical correctness. It covers not only support vector machines but also less known techniques for kernel-based regression, outlier detection, clustering and dimensionality reduction. Additionally, it offers an outlook on recent developments in kernel methods that have not yet made it into the regular textbooks: structured prediction, dependency estimation and learning of the kernel function. Each topic is illustrated with examples of successful application in the computer vision literature, making Kernel Methods in Computer Vision a useful guide not only for those wanting to understand the working principles of kernel methods, but also for anyone wanting to apply them to real-life problems.

Download or read book Scalable Kernel Methods and Algorithms for General Sequence Analysis written by Pavel Kuksa and published by . This book was released on 2011 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of large-scale sequential data has become an important task in machine learning and pattern recognition, inspired in part by numerous scientific and technological applications such as the document and text classification or the analysis of biological sequences. However, current computational methods for sequence comparison still lack accuracy and scalability necessary for reliable analysis of large datasets. To this end, we develop a new framework (efficient algorithms and methods) that solve sequence matching, comparison, classification, and pattern extraction problems in linear time, with increased accuracy, improving over the prior art. In particular, we propose novel ways of modeling sequences under complex transformations (such as multiple insertions, deletions, mutations) and present a new family of similarity measures (kernels), the spatial string kernels (SSK). SSKs can be computed very efficiently and perform better than the best available methods on a variety of distinct classification tasks. We also present new algorithms for approximate (e.g., with mismatches) string comparison that improve currently known time complexity bounds for such tasks and show order-of-magnitude running time improvements. We then propose novel linear time algorithms for representative pattern extraction in sequence data sets that exploit developed computational framework. In an extensive set of experiments on many challenging classification problems, such as detecting homology (evolutionary similarity) of remotely related proteins, categorizing texts, and performing classification of music samples, our algorithms and similarity measures display state-of-the-art classification performance and run significantly faster than existing methods.

Download or read book Deep Learning Innovations and Their Convergence With Big Data written by Karthik, S. and published by IGI Global. This book was released on 2017-07-13 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics. Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics.

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 Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code