Nearest Neighbor Classification in Infinite Dimension
Author : Frederic Cérou
Publisher :
Page : pages
File Size : 30,38 MB
Release : 2005
Category :
ISBN :
Author : Frederic Cérou
Publisher :
Page : pages
File Size : 30,38 MB
Release : 2005
Category :
ISBN :
Author : Gérard Biau
Publisher : Springer
Page : 284 pages
File Size : 27,44 MB
Release : 2015-12-08
Category : Mathematics
ISBN : 3319253883
This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods. Gérard Biau is a professor at Université Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).
Author : John Mallet-Paret
Publisher : Springer Science & Business Media
Page : 495 pages
File Size : 37,33 MB
Release : 2012-10-11
Category : Mathematics
ISBN : 1461445221
This collection covers a wide range of topics of infinite dimensional dynamical systems generated by parabolic partial differential equations, hyperbolic partial differential equations, solitary equations, lattice differential equations, delay differential equations, and stochastic differential equations. Infinite dimensional dynamical systems are generated by evolutionary equations describing the evolutions in time of systems whose status must be depicted in infinite dimensional phase spaces. Studying the long-term behaviors of such systems is important in our understanding of their spatiotemporal pattern formation and global continuation, and has been among major sources of motivation and applications of new developments of nonlinear analysis and other mathematical theories. Theories of the infinite dimensional dynamical systems have also found more and more important applications in physical, chemical, and life sciences. This book collects 19 papers from 48 invited lecturers to the International Conference on Infinite Dimensional Dynamical Systems held at York University, Toronto, in September of 2008. As the conference was dedicated to Professor George Sell from University of Minnesota on the occasion of his 70th birthday, this collection reflects the pioneering work and influence of Professor Sell in a few core areas of dynamical systems, including non-autonomous dynamical systems, skew-product flows, invariant manifolds theory, infinite dimensional dynamical systems, approximation dynamics, and fluid flows.
Author : Hassan Doosti
Publisher : Springer Nature
Page : 309 pages
File Size : 20,55 MB
Release :
Category :
ISBN : 3031665015
Author : Hans Ulrich Simon
Publisher : Springer
Page : 667 pages
File Size : 40,63 MB
Release : 2006-09-29
Category : Computers
ISBN : 3540352961
This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA, June 2006. The book presents 43 revised full papers together with 2 articles on open problems and 3 invited lectures. The papers cover a wide range of topics including clustering, un- and semi-supervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, and more.
Author : Lech Polkowski
Publisher : Springer
Page : 461 pages
File Size : 48,57 MB
Release : 2015-04-06
Category : Technology & Engineering
ISBN : 3319128809
This book presents a study in knowledge discovery in data with knowledge understood as a set of relations among objects and their properties. Relations in this case are implicative decision rules and the paradigm in which they are induced is that of computing with granules defined by rough inclusions, the latter introduced and studied within rough mereology, the fuzzified version of mereology. In this book basic classes of rough inclusions are defined and based on them methods for inducing granular structures from data are highlighted. The resulting granular structures are subjected to classifying algorithms, notably k—nearest neighbors and bayesian classifiers. Experimental results are given in detail both in tabular and visualized form for fourteen data sets from UCI data repository. A striking feature of granular classifiers obtained by this approach is that preserving the accuracy of them on original data, they reduce substantially the size of the granulated data set as well as the set of granular decision rules. This feature makes the presented approach attractive in cases where a small number of rules providing a high classification accuracy is desirable. As basic algorithms used throughout the text are explained and illustrated with hand examples, the book may also serve as a textbook.
Author : Ke-Lin Du
Publisher : Springer Nature
Page : 988 pages
File Size : 23,16 MB
Release : 2019-09-12
Category : Mathematics
ISBN : 1447174526
This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Author : Lior Rokach
Publisher : Springer Nature
Page : 975 pages
File Size : 42,16 MB
Release : 2023-08-17
Category : Computers
ISBN : 3031246284
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.
Author : Uwe Franz
Publisher : World Scientific
Page : 547 pages
File Size : 11,23 MB
Release : 2005-01-12
Category : Mathematics
ISBN : 9814481025
This volume collects research papers in quantum probability and related fields and reflects the recent developments in quantum probability ranging from the foundations to its applications.
Author : George H. Chen
Publisher : Foundations and Trends (R) in Machine Learning
Page : 264 pages
File Size : 12,52 MB
Release : 2018-05-30
Category :
ISBN : 9781680834543
Explains the success of Nearest Neighbor Methods in Prediction, both in theory and in practice.