Kernel Mean Embedding Of Distributions

DOWNLOAD

Download Kernel Mean Embedding Of Distributions PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Kernel Mean Embedding Of Distributions book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Kernel Mean Embedding Of Distributions

DOWNLOAD
Author : Krikamol Muandet
language : en
Publisher:
Release Date : 2017

Kernel Mean Embedding Of Distributions written by Krikamol Muandet and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Hilbert space categories.

A Hilbert space embedding of a distribution--in short, a kernel mean embedding--has recently emerged as a powerful tool for machine learning and statistical inference. The basic idea behind this framework is to map distributions into a reproducing kernel Hilbert space (RKHS) in which the whole arsenal of kernel methods can be extended to probability measures. It can be viewed as a generalization of the original "feature map" common to support vector machines (SVMs) and other kernel methods. In addition to the classical applications of kernel methods, the kernel mean embedding has found novel applications in fields ranging from probabilistic modeling to statistical inference, causal discovery, and deep learning. This survey aims to give a comprehensive review of existing work and recent advances in this research area, and to discuss challenging issues and open problems that could potentially lead to new research directions. The survey begins with a brief introduction to the RKHS and positive definite kernels which forms the backbone of this survey, followed by a thorough discussion of the Hilbert space embedding of marginal distributions, theoretical guarantees, and a review of its applications. The embedding of distributions enables us to apply RKHS methods to probability measures which prompts a wide range of applications such as kernel two-sample testing, independent testing, and learning on distributional data. Next, we discuss the Hilbert space embedding for conditional distributions, give theoretical insights, and review some applications. The conditional mean embedding enables us to perform sum, product, and Bayes' rules--which are ubiquitous in graphical model, probabilistic inference, and reinforcement learning-- in a non-parametric way using this new representation of distributions. We then discuss relationships between this framework and other related areas. Lastly, we give some suggestions on future research directions.

Kernel Mean Embedding Of Distributions

DOWNLOAD
Author : Krikamol Muandet
language : en
Publisher:
Release Date : 2017-06-28

Kernel Mean Embedding Of Distributions written by Krikamol Muandet and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-28 with Computers categories.

Provides a comprehensive review of kernel mean embeddings of distributions and, in the course of doing so, discusses some challenging issues that could potentially lead to new research directions. The targeted audience includes graduate students and researchers in machine learning and statistics.

Hyperspectral Image Analysis

DOWNLOAD
Author : Saurabh Prasad
language : en
Publisher: Springer Nature
Release Date : 2020-04-27

Hyperspectral Image Analysis written by Saurabh Prasad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-27 with Computers categories.

This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.

Advances In Dynamics Optimization And Computation

DOWNLOAD
Author : Oliver Junge
language : en
Publisher: Springer Nature
Release Date : 2020-07-20

Advances In Dynamics Optimization And Computation written by Oliver Junge and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-20 with Technology & Engineering categories.

This book presents a collection of papers on recent advances in problems concerning dynamics, optimal control and optimization. In many chapters, computational techniques play a central role. Set-oriented techniques feature prominently throughout the book, yielding state-of-the-art algorithms for computing general invariant sets, constructing globally optimal controllers and solving multi-objective optimization problems.

Probabilistic Machine Learning

DOWNLOAD
Author : Kevin P. Murphy
language : en
Publisher: MIT Press
Release Date : 2023-08-15

Probabilistic Machine Learning written by Kevin P. Murphy and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-15 with Computers categories.

An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning. Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributions Explores how to use probabilistic models and inference for causal inference and decision making Features online Python code accompaniment

Foundations Of Modern Statistics

DOWNLOAD
Author : Denis Belomestny
language : en
Publisher: Springer Nature
Release Date : 2023-07-16

Foundations Of Modern Statistics written by Denis Belomestny and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-16 with Mathematics categories.

This book contains contributions from the participants of the international conference “Foundations of Modern Statistics” which took place at Weierstrass Institute for Applied Analysis and Stochastics (WIAS), Berlin, during November 6–8, 2019, and at Higher School of Economics (HSE University), Moscow, during November 30, 2019. The events were organized in honor of Professor Vladimir Spokoiny on the occasion of his 60th birthday. Vladimir Spokoiny has pioneered the field of adaptive statistical inference and contributed to a variety of its applications. His more than 30 years of research in the field of mathematical statistics had a great influence on the development of the mathematical theory of statistics to its present state. It has inspired many young researchers to start their research in this exciting field of mathematics. The papers contained in this book reflect the broad field of interests of Vladimir Spokoiny: optimal rates and non-asymptotic bounds in nonparametrics, Bayes approaches from a frequentist point of view, optimization, signal processing, and statistical theory motivated by models in applied fields. Materials prepared by famous scientists contain original scientific results, which makes the publication valuable for researchers working in these fields. The book concludes by a conversation of Vladimir Spokoiny with Markus Reiβ and Enno Mammen. This interview gives some background on the life of Vladimir Spokoiny and his many scientific interests and motivations.

Regularization Optimization Kernels And Support Vector Machines

DOWNLOAD
Author : Johan A.K. Suykens
language : en
Publisher: CRC Press
Release Date : 2014-10-23

Regularization Optimization Kernels And Support Vector Machines written by Johan A.K. Suykens and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-23 with Computers categories.

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vecto

Cause Effect Pairs In Machine Learning

DOWNLOAD
Author : Isabelle Guyon
language : en
Publisher: Springer Nature
Release Date : 2019-10-22

Cause Effect Pairs In Machine Learning written by Isabelle Guyon and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-22 with Computers categories.

This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website. Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.

Recurrence Plots And Their Quantifications Methodological Breakthroughs And Interdisciplinary Discoveries

DOWNLOAD
Author : Yoshito Hirata
language : en
Publisher: Springer Nature
Release Date : 2025-08-30

Recurrence Plots And Their Quantifications Methodological Breakthroughs And Interdisciplinary Discoveries written by Yoshito Hirata and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-30 with Science categories.

This book collects selected contributions from the 10th Recurrence Plot Symposium, held at the University of Tsukuba, Japan (August 28-30, 2023). It reflects on the achievements of the previous nine symposia while exploring future prospects of recurrence plots and their quantifications in combination with other key research strategies. The book provides a comprehensive bibliographic overview of the methodology, offering a solid foundation for both new and experienced researchers. It discusses the mathematical foundations of recurrence plots and recurrence microstates and links them to future directions, such as machine learning, highlighting the potential for innovative applications. The text also presents an alternative approach to representing recurrences, offering fresh perspectives on this well-established technique. Additionally, it explores the two-dimensional projections of three-dimensional chromosomal structures as dot maps, opening the door to unique chromosomal descriptions in terms of recurrence quantifiers. Numerous other contributed chapters highlight the active development of this method and its clever applications to real-world systems. The book is intended for researchers working in various fields such as physics, statistics, and mathematics, with its interdisciplinary applications also benefiting experts from psychology, physiology, geosciences, finance, and engineering.

Matrix And Operator Equations And Applications

DOWNLOAD
Author : Mohammad Sal Moslehian
language : en
Publisher: Springer Nature
Release Date : 2023-07-29

Matrix And Operator Equations And Applications written by Mohammad Sal Moslehian and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-29 with Mathematics categories.

This book concerns matrix and operator equations that are widely applied in various disciplines of science to formulate challenging problems and solve them in a faithful way. The main aim of this contributed book is to study several important matrix and operator equalities and equations in a systematic and self-contained fashion. Some powerful methods have been used to investigate some significant equations in functional analysis, operator theory, matrix analysis, and numerous subjects in the last decades. The book is divided into two parts: (I) Matrix Equations and (II) Operator Equations. In the first part, the state-of-the-art of systems of matrix equations is given and generalized inverses are used to find their solutions. The semi-tensor product of matrices is used to solve quaternion matrix equations. The contents of some chapters are related to the relationship between matrix inequalities, matrix means, numerical range, and matrix equations. In addition, quaternion algebras and their applications are employed in solving some famous matrix equations like Sylvester, Stein, and Lyapunov equations. A chapter devoted to studying Hermitian polynomial matrix equations, which frequently arise from linear-quadratic control problems. Moreover, some classical and recently discovered inequalities for matrix exponentials are reviewed. In the second part, the latest developments in solving several equations appearing in modern operator theory are demonstrated. These are of interest to a wide audience of pure and applied mathematicians. For example, the Daugavet equation in the linear and nonlinear setting, iterative processes and Volterra-Fredholm integral equations, semicircular elements induced by connected finite graphs, free probability, singular integral operators with shifts, and operator differential equations closely related to the properties of the coefficient operators in some equations are discussed. The chapters give a comprehensive account of their subjects. The exhibited chapters are written in a reader-friendly style and can be read independently. Each chapter contains a rich bibliography. This book is intended for use by both researchers and graduate students of mathematics, physics, and engineering.