Explaining The Success Of Nearest Neighbor Methods In Prediction
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Explaining The Success Of Nearest Neighbor Methods In Prediction
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Author : George H. Chen
language : en
Publisher:
Release Date : 2018
Explaining The Success Of Nearest Neighbor Methods In Prediction written by George H. Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Forecasting categories.
Many modern methods for prediction leverage nearest neighbor search to find past training examples most similar to a test example, an idea that dates back in text to at least the 11th century and has stood the test of time. This monograph aims to explain the success of these methods, both in theory, for which we cover foundational nonasymptotic statistical guarantees on nearest-neighbor-based regression and classification, and in practice, for which we gather prominent methods for approximate nearest neighbor search that have been essential to scaling prediction systems reliant on nearest neighbor analysis to handle massive datasets. Furthermore, we discuss connections to learning distances for use with nearest neighbor methods, including how random decision trees and ensemble methods learn nearest neighbor structure, as well as recent developments in crowdsourcing and graphons. In terms of theory, our focus is on nonasymptotic statistical guarantees, which we state in the form of how many training data and what algorithm parameters ensure that a nearest neighbor prediction method achieves a user-specified error tolerance. We begin with the most general of such results for nearest neighbor and related kernel regression and classification in general metric spaces. In such settings in which we assume very little structure, what enables successful prediction is smoothness in the function being estimated for regression, and a low probability of landing near the decision boundary for classification. In practice, these conditions could be difficult to verify empirically for a real dataset. We then cover recent theoretical guarantees on nearest neighbor prediction in the three case studies of time series forecasting, recommending products to people over time, and delineating human organs in medical images by looking at image patches. In these case studies, clustering structure, which is easier to verify in data and more readily interpretable by practitioners, enables successful prediction.
Inference And Learning From Data
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Author : Ali H. Sayed
language : en
Publisher: Cambridge University Press
Release Date : 2022-12-22
Inference And Learning From Data written by Ali H. Sayed 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 2022-12-22 with Computers categories.
Discover data-driven learning methods with the third volume of this extraordinary three-volume set.
Beyond The Worst Case Analysis Of Algorithms
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Author : Tim Roughgarden
language : en
Publisher: Cambridge University Press
Release Date : 2021-01-14
Beyond The Worst Case Analysis Of Algorithms written by Tim Roughgarden 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 2021-01-14 with Computers categories.
Introduces exciting new methods for assessing algorithms for problems ranging from clustering to linear programming to neural networks.
Learning Theory From First Principles
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Author : Francis Bach
language : en
Publisher: MIT Press
Release Date : 2024-12-24
Learning Theory From First Principles written by Francis Bach and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-24 with Computers categories.
A comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory. Research has exploded in the field of machine learning resulting in complex mathematical arguments that are hard to grasp for new comers. . In this accessible textbook, Francis Bach presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures. Taking the position that learning theory does not exist outside of algorithms that can be run in practice, this book focuses on the theoretical analysis of learning algorithms as it relates to their practical performance. Bach provides the simplest formulations that can be derived from first principles, constructing mathematically rigorous results and proofs without overwhelming students. Provides a balanced and unified treatment of most prevalent machine learning methods Emphasizes practical application and features only commonly used algorithmic frameworks Covers modern topics not found in existing texts, such as overparameterized models and structured prediction Integrates coverage of statistical theory, optimization theory, and approximation theory Focuses on adaptivity, allowing distinctions between various learning techniques Hands-on experiments, illustrative examples, and accompanying code link theoretical guarantees to practical behaviors
Mathematical Analysis In Interdisciplinary Research
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Author : Ioannis N. Parasidis
language : en
Publisher: Springer Nature
Release Date : 2022-03-10
Mathematical Analysis In Interdisciplinary Research written by Ioannis N. Parasidis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-10 with Mathematics categories.
This contributed volume provides an extensive account of research and expository papers in a broad domain of mathematical analysis and its various applications to a multitude of fields. Presenting the state-of-the-art knowledge in a wide range of topics, the book will be useful to graduate students and researchers in theoretical and applicable interdisciplinary research. The focus is on several subjects including: optimal control problems, optimal maintenance of communication networks, optimal emergency evacuation with uncertainty, cooperative and noncooperative partial differential systems, variational inequalities and general equilibrium models, anisotropic elasticity and harmonic functions, nonlinear stochastic differential equations, operator equations, max-product operators of Kantorovich type, perturbations of operators, integral operators, dynamical systems involving maximal monotone operators, the three-body problem, deceptive systems, hyperbolic equations, strongly generalized preinvex functions, Dirichlet characters, probability distribution functions, applied statistics, integral inequalities, generalized convexity, global hyperbolicity of spacetimes, Douglas-Rachford methods, fixed point problems, the general Rodrigues problem, Banach algebras, affine group, Gibbs semigroup, relator spaces, sparse data representation, Meier-Keeler sequential contractions, hybrid contractions, and polynomial equations. Some of the works published within this volume provide as well guidelines for further research and proposals for new directions and open problems.
Machine Learning For Data Science Handbook
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Author : Lior Rokach
language : en
Publisher: Springer Nature
Release Date : 2023-08-17
Machine Learning For Data Science Handbook written by Lior Rokach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-17 with Computers categories.
This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
Explaining The Success Of Nearest Neighbor Methods In Prediction
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Author : George H. Chen
language : en
Publisher: Foundations and Trends (R) in Machine Learning
Release Date : 2018-05-30
Explaining The Success Of Nearest Neighbor Methods In Prediction written by George H. Chen and has been published by Foundations and Trends (R) in Machine Learning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-30 with categories.
Explains the success of Nearest Neighbor Methods in Prediction, both in theory and in practice.
Why Machines Learn
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Author : Anil Ananthaswamy
language : en
Publisher: Random House
Release Date : 2024-07-16
Why Machines Learn written by Anil Ananthaswamy and has been published by Random House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-16 with Computers categories.
'An invaluable companion for anyone who wants a deep understanding of what’s under the hood of often inscrutable machines' Melanie Mitchell A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence Machine-learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumour is cancerous, or deciding whether someone gets bail. They now influence discoveries in chemistry, biology and physics - the study of genomes, extra-solar planets, even the intricacies of quantum systems. We are living through a revolution in artificial intelligence that is not slowing down. This major shift is based on simple mathematics, some of which goes back centuries: linear algebra and calculus, the stuff of eighteenth-century mathematics. Indeed by the mid-1850s, a lot of the groundwork was all done. It took the development of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see all around us today. In this enlightening book, Anil Ananthaswamy explains the fundamental maths behind AI, which suggests that the basics of natural and artificial intelligence might follow the same mathematical rules. As Ananthaswamy resonantly concludes, to make the most of our most wondrous technologies we need to understand their profound limitations - the clues lie in the maths that makes AI possible.
Machine Learning In Vlsi Computer Aided Design
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Author : Ibrahim (Abe) M. Elfadel
language : en
Publisher: Springer
Release Date : 2019-03-15
Machine Learning In Vlsi Computer Aided Design written by Ibrahim (Abe) M. Elfadel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-15 with Technology & Engineering categories.
This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure thatI recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center
Pattern Recognition Icpr International Workshops And Challenges
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Author : Alberto Del Bimbo
language : en
Publisher: Springer Nature
Release Date : 2021-02-20
Pattern Recognition Icpr International Workshops And Challenges written by Alberto Del Bimbo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-20 with Computers categories.
This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020.