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About 10,000 papers for “kernel methods”

Kernel Methods

Series in machine perception and artificial intelligence · 2010

Cited by 0

Kernel methods

Martin Sewell · 2009

The term kernel is derived from a word that can be traced back to c. 1000 and originally meant a seed (contained within a fruit) or the softer (usually edible) part contained within the hard shell of a nut or stone-fruit. The former meaning is now obsolete. It was first used in mathematics when it was defined for integral equations in which the kernel is known and the other function(s) unknown, but now has several meanings in maths. The machine learning term kernel trick was first used in 1998. 2

Cited by 0

Kernel methods

William W. Hsieh · Cambridge University Press eBooks · 2009

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Cited by 6

Kernel methods

Max Pinheiro, Pavlo O. Dral · Elsevier eBooks · 2022

Cited by 8Open access

Kernel Methods

Johan A. K. Suykens · Elsevier eBooks · 2009

Cited by 6

Kernel Methods

Mohammed J. Zaki, Wagner Meira, Jr · Cambridge University Press eBooks · 2018

The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.

Cited by 0

Kernel Methods

Xinhua Zhang · Encyclopedia of Machine Learning and Data Mining · 2016

Cited by 0

Kernel Methods

Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos · Cambridge University Press eBooks · 2020

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.

Cited by 0

Kernel Methods

Nello Cristianini, John Shawe‐Taylor, Craig Saunders · IGI Global eBooks · 2011

During the past decade, a major revolution has taken place in pattern-recognition technology with the introduction of rigorous and powerful mathematical approaches in problem domains previously treated with heuristic and less efficient techniques. The use of convex optimisation and statistical learning theory has been combined with ideas from functional analysis and classical statistics to produce a class of algorithms called kernel methods (KMs), which have rapidly become commonplace in applications. This book, and others, provides evidence of the practical applications that have made kernel methods a fundamental part of the toolbox for machine learning, statistics, and signal processing practitioners. The field of kernel methods has not only provided new insights and therefore new algorithms, but it has also created much discussion on well-established techniques such as Parzen windows and Gaussian processes, which use essentially the same technique but in different frameworks.Request access from your librarian to read this chapter's full text.

Cited by 1

Kernel Methods

Ali H. Sayed · Cambridge University Press eBooks · 2022

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 final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, 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, data and inference.

Cited by 0

Kernel Methods

Xinhua Zhang · Encyclopedia of Machine Learning · 2010

Cited by 1

Kernel Methods

Schölkopf, B. · MPG.PuRe (Max Planck Society) · 2009

Elementary Properties of PD KernelsKernels from Feature Maps.If maps X into a dot product space H, then (x), (x ) is a pd kernel on X X.Positivity on the Diagonal.k(x, x) 0 for all x X Cauchy-Schwarz Inequality.k(x, x ) 2 k(x, x)k(x , x ) (Hint: compute the determinant of the Gram matrix)Vanishing Diagonals.k(x, x) = 0 for all x X = k(x, x ) = 0 for all x, x X

Cited by 0Open access

Kernel Methods

William W. Hsieh · Cambridge University Press eBooks · 2023

Kernel methods provide an alternative family of non-linear methods to neural networks, with support vector machine being the best known among kernel methods. Almost all linear statistical methods have been non-linearly generalized by the kernel approach, including ridge regression, linear discriminant analysis, principal component analysis, canonical correlation analysis, and so on. The kernel method has also been extended to probabilisitic models, for example Gaussian processes.

Cited by 0