Download or read book An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces written by Sergei Pereverzyev and published by Springer Nature. This book was released on 2022-05-17 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks. The author demonstrates how the concept of reproducing kernel Hilbert Spaces (RKHS), accompanied with tools from regularization theory, can be effectively used in the design and justification of kernel learning algorithms, which can address problems in several areas of artificial intelligence. Also provided is a detailed description of two biomedical applications of the considered algorithms, demonstrating how close the theory is to being practically implemented. Among the book’s several unique features is its analysis of a large class of algorithms of the Learning Theory that essentially comprise every linear regularization scheme, including Tikhonov regularization as a specific case. It also provides a methodology for analyzing not only different supervised learning problems, such as regression or ranking, but also different learning scenarios, such as unsupervised domain adaptation or reinforcement learning. By analyzing these topics using the same theoretical framework, rather than approaching them separately, their presentation is streamlined and made more approachable. An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces is an ideal resource for graduate and postgraduate courses in computational mathematics and data science.

Download or read book An Introduction to the Theory of Reproducing Kernel Hilbert Spaces written by Vern I. Paulsen and published by Cambridge University Press. This book was released on 2016-04-11 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reproducing kernel Hilbert spaces have developed into an important tool in many areas, especially statistics and machine learning, and they play a valuable role in complex analysis, probability, group representation theory, and the theory of integral operators. This unique text offers a unified overview of the topic, providing detailed examples of applications, as well as covering the fundamental underlying theory, including chapters on interpolation and approximation, Cholesky and Schur operations on kernels, and vector-valued spaces. Self-contained and accessibly written, with exercises at the end of each chapter, this unrivalled treatment of the topic serves as an ideal introduction for graduate students across mathematics, computer science, and engineering, as well as a useful reference for researchers working in functional analysis or its applications.

Download or read book MICAI 2007 Advances in Artificial Intelligence written by Alexander Gelbukh and published by Springer. This book was released on 2007-10-24 with total page 1255 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th Mexican International Conference on Artificial Intelligence, MICAI 2007, held in Aguascalientes, Mexico, in November 2007. The 116 revised full papers presented were carefully reviewed and selected from numerous submissions for inclusion in the book. The papers are organized in sections on topics that include computational intelligence, neural networks, knowledge representation and reasoning, agents and multiagent systems.

Download or read book Machine Learning written by Sergios Theodoridis and published by Academic Press. This book was released on 2020-02-19 with total page 1160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python. The chapters are written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as courses on sparse modeling, deep learning, and probabilistic graphical models. New to this edition: Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition. The chapter, starting from the basic perceptron and feed-forward neural networks concepts, now presents an in depth treatment of deep networks, including recent optimization algorithms, batch normalization, regularization techniques such as the dropout method, convolutional neural networks, recurrent neural networks, attention mechanisms, adversarial examples and training, capsule networks and generative architectures, such as restricted Boltzman machines (RBMs), variational autoencoders and generative adversarial networks (GANs). Expanded treatment of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes. Presents the physical reasoning, mathematical modeling and algorithmic implementation of each method Updates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling Provides case studies on a variety of topics, including protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, and more

Download or read book Reproducing Kernel Spaces and Applications written by Daniel Alpay and published by Birkhäuser. This book was released on 2012-12-06 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: The notions of positive functions and of reproducing kernel Hilbert spaces play an important role in various fields of mathematics, such as stochastic processes, linear systems theory, operator theory, and the theory of analytic functions. Also they are relevant for many applications, for example to statistical learning theory and pattern recognition. The present volume contains a selection of papers which deal with different aspects of reproducing kernel Hilbert spaces. Topics considered include one complex variable theory, differential operators, the theory of self-similar systems, several complex variables, and the non-commutative case. The book is of interest to a wide audience of pure and applied mathematicians, electrical engineers and theoretical physicists.

Download or read book A Primer on Reproducing Kernel Hilbert Spaces written by Jonathan H. Manton and published by . This book was released on 2015-11-20 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hilbert space theory is an invaluable mathematical tool in numerous signal processing and systems theory applications. Hilbert spaces satisfying certain additional properties are known as Reproducing Kernel Hilbert Spaces (RKHSs). This primer gives a gentle and novel introduction to RKHS theory. It also presents several classical applications. It concludes by focusing on recent developments in the machine learning literature concerning embeddings of random variables. Parenthetical remarks are used to provide greater technical detail, which some readers may welcome, but they may be ignored without compromising the cohesion of the primer. Proofs are there for those wishing to gain experience at working with RKHSs; simple proofs are preferred to short, clever, but otherwise uninformative proofs. Italicised comments appearing in proofs provide intuition or orientation or both. A Primer on Reproducing Kernel Hilbert Spaces empowers readers to recognize when and how RKHS theory can profit them in their own work.

Download or read book Learning with Kernels written by Bernhard Scholkopf and published by MIT Press. This book was released on 2018-06-05 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Download or read book Artificial Intelligence Big Data and Data Science in Statistics written by Ansgar Steland and published by Springer Nature. This book was released on 2022-11-15 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book’s expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and data scientists, as well as engineers and computer scientists working in related fields or applications.

Download or read book Essentials of Pattern Recognition written by Jianxin Wu and published by Cambridge University Press. This book was released on 2020-11-19 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible undergraduate introduction to the concepts and methods in pattern recognition, machine learning and deep learning.

Download or read book Selected Applications of Convex Optimization written by Li Li and published by Springer. This book was released on 2015-03-26 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems. All derivation processes are presented in detail to aid in comprehension. The book offers concrete guidance, helping readers recognize and formulate convex optimization problems they might encounter in practice.

Download or read book Artificial Intelligence Applications and Innovations written by Harris Papadopoulos and published by Springer. This book was released on 2013-09-03 with total page 733 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2013, held in Paphos, Cyprus, in September/October 2013. The 26 revised full papers presented together with a keynote speech at the main event and 44 papers of 8 collocated workshops were carefully reviewed and selected for inclusion in the volume. The papers of the main event are organized in topical sections on data mining, medical informatics and biomedical engineering, problem solving and scheduling, modeling and decision support systems, robotics, and intelligent signal and image processing.

Download or read book Algorithmic Learning Theory written by Sanjay Jain and published by Springer. This book was released on 2013-09-27 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 24th International Conference on Algorithmic Learning Theory, ALT 2013, held in Singapore in October 2013, and co-located with the 16th International Conference on Discovery Science, DS 2013. The 23 papers presented in this volume were carefully reviewed and selected from 39 submissions. In addition the book contains 3 full papers of invited talks. The papers are organized in topical sections named: online learning, inductive inference and grammatical inference, teaching and learning from queries, bandit theory, statistical learning theory, Bayesian/stochastic learning, and unsupervised/semi-supervised learning.

Download or read book Elements of Dimensionality Reduction and Manifold Learning written by Benyamin Ghojogh and published by Springer Nature. This book was released on 2023-02-02 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and nonlinear dimensionality reduction and manifold learning. Three main aspects of dimensionality reduction are covered: spectral dimensionality reduction, probabilistic dimensionality reduction, and neural network-based dimensionality reduction, which have geometric, probabilistic, and information-theoretic points of view to dimensionality reduction, respectively. The necessary background and preliminaries on linear algebra, optimization, and kernels are also explained to ensure a comprehensive understanding of the algorithms. The tools introduced in this book can be applied to various applications involving feature extraction, image processing, computer vision, and signal processing. This book is applicable to a wide audience who would like to acquire a deep understanding of the various ways to extract, transform, and understand the structure of data. The intended audiences are academics, students, and industry professionals. Academic researchers and students can use this book as a textbook for machine learning and dimensionality reduction. Data scientists, machine learning scientists, computer vision scientists, and computer scientists can use this book as a reference. It can also be helpful to statisticians in the field of statistical learning and applied mathematicians in the fields of manifolds and subspace analysis. Industry professionals, including applied engineers, data engineers, and engineers in various fields of science dealing with machine learning, can use this as a guidebook for feature extraction from their data, as the raw data in industry often require preprocessing. The book is grounded in theory but provides thorough explanations and diverse examples to improve the reader’s comprehension of the advanced topics. Advanced methods are explained in a step-by-step manner so that readers of all levels can follow the reasoning and come to a deep understanding of the concepts. This book does not assume advanced theoretical background in machine learning and provides necessary background, although an undergraduate-level background in linear algebra and calculus is recommended.

Download or read book Information Systems Architecture and Technology Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology ISAT 2019 written by Jerzy Świątek and published by Springer Nature. This book was released on 2019-09-04 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume book highlights significant advances in the development of new information systems technologies and architectures. Further, it helps readers solve specific research and analytical problems and glean useful knowledge and business value from data. Each chapter provides an analysis of a specific technical problem, followed by a numerical analysis, simulation, and implementation of the solution to the real-world problem. Managing an organization, especially in today’s rapidly changing environment, is a highly complex process. Increased competition in the marketplace, especially as a result of the massive and successful entry of foreign businesses into domestic markets, changes in consumer behaviour, and broader access to new technologies and information, calls for organisational restructuring and the introduction and modification of management methods using the latest scientific advances. This situation has prompted various decision-making bodies to introduce computer modelling of organization management systems. This book presents the peer-reviewed proceedings of the 40th Anniversary International Conference “Information Systems Architecture and Technology” (ISAT), held on September 15–17, 2019, in Wrocław, Poland. The conference was organised by the Computer Science Department, Faculty of Computer Science and Management, Wroclaw University of Sciences and Technology, and University of Applied Sciences in Nysa, Poland. The papers have been grouped into three major sections: Part I—discusses topics including, but not limited to, artificial intelligence methods, knowledge discovery and data mining, big data, knowledge-based management, Internet of Things, cloud computing and high-performance computing, distributed computer systems, content delivery networks, and service-oriented computing. Part II—addresses various topics, such as system modelling for control, recognition and decision support, mathematical modelling in computer system design, service-oriented systems, and cloud computing, and complex process modelling. Part III—focuses on a number of themes, like knowledge-based management, modelling of financial and investment decisions, modelling of managerial decisions, production systems management, and maintenance, risk management, small business management, and theories and models of innovation.

Download or read book PRICAI 2023 Trends in Artificial Intelligence written by Fenrong Liu and published by Springer Nature. This book was released on 2023-11-10 with total page 515 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set, LNCS 14325-14327 constitutes the thoroughly refereed proceedings of the 20th Pacific Rim Conference on Artificial Intelligence, PRICAI 2023, held in Jakarta, Indonesia, in November 2023. The 95 full papers and 36 short papers presented in these volumes were carefully reviewed and selected from 422 submissions. PRICAI covers a wide range of topics in the areas of social and economic importance for countries in the Pacific Rim: artificial intelligence, machine learning, natural language processing, knowledge representation and reasoning, planning and scheduling, computer vision, distributed artificial intelligence, search methodologies, etc.

Download or read book Exercises in Applied Mathematics written by Daniel Alpay and published by Springer Nature. This book was released on with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book ECAI 2010 written by European Coordinating Committee for Artificial Intelligence and published by IOS Press. This book was released on 2010 with total page 1184 pages. Available in PDF, EPUB and Kindle. Book excerpt: LC copy bound in 2 v.: v. 1, p. 1-509; v. 2, p. [509]-1153.