An Introduction To Artificial Intelligence Based On Reproducing Kernel Hilbert Spaces

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An Introduction To Artificial Intelligence Based On Reproducing Kernel Hilbert Spaces

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Author : Sergei Pereverzyev
language : en
Publisher:
Release Date : 2022

An Introduction To Artificial Intelligence Based On Reproducing Kernel Hilbert Spaces written by Sergei Pereverzyev and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

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.

An Introduction To Artificial Intelligence Based On Reproducing Kernel Hilbert Spaces

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Author : Sergei Pereverzyev
language : en
Publisher: Springer Nature
Release Date : 2022-05-17

An Introduction To Artificial Intelligence Based On Reproducing Kernel Hilbert Spaces written by Sergei Pereverzyev and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-17 with Mathematics categories.

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.

Micai 2007 Advances In Artificial Intelligence

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Author : Alexander Gelbukh
language : en
Publisher: Springer
Release Date : 2007-10-24

Micai 2007 Advances In Artificial Intelligence written by Alexander Gelbukh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-24 with Computers categories.

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.

Machine Learning

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Author : Sergios Theodoridis
language : en
Publisher: Academic Press
Release Date : 2020-02-19

Machine Learning written by Sergios Theodoridis and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-19 with Technology & Engineering categories.

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

Artificial Intelligence Big Data And Data Science In Statistics

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Author : Ansgar Steland
language : en
Publisher: Springer Nature
Release Date : 2022-11-15

Artificial Intelligence Big Data And Data Science In Statistics written by Ansgar Steland and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-15 with Mathematics categories.

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.

Essentials Of Pattern Recognition

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Author : Jianxin Wu
language : en
Publisher: Cambridge University Press
Release Date : 2020-11-19

Essentials Of Pattern Recognition written by Jianxin Wu and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-19 with Computers categories.

An accessible undergraduate introduction to the concepts and methods in pattern recognition, machine learning and deep learning.

Artificial Intelligence Applications And Innovations

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Author : Harris Papadopoulos
language : en
Publisher: Springer
Release Date : 2013-09-03

Artificial Intelligence Applications And Innovations written by Harris Papadopoulos and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-03 with Computers categories.

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.

Selected Applications Of Convex Optimization

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Author : Li Li
language : en
Publisher: Springer
Release Date : 2015-03-26

Selected Applications Of Convex Optimization written by Li Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-26 with Business & Economics categories.

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.

Algorithmic Learning Theory

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Author : Sanjay Jain
language : en
Publisher: Springer
Release Date : 2013-09-27

Algorithmic Learning Theory written by Sanjay Jain and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-27 with Computers categories.

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.

Elements Of Dimensionality Reduction And Manifold Learning

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Author : Benyamin Ghojogh
language : en
Publisher: Springer Nature
Release Date : 2023-02-02

Elements Of Dimensionality Reduction And Manifold Learning written by Benyamin Ghojogh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-02 with Computers categories.

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.