Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, Vapnik-Chervonenkis theory, epsilon entropy, parametric classification, error estimation, free classifiers, and neural networks. Wherever possible, distribution-free properties and inequalities are derived. A substantial portion...
Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. Th...
Density estimation has evolved enormously since the days of bar plots and histograms, but researchers and users are still struggling with the problem of the selection of the bin widths. This text explores a new paradigm for the data-based or automatic selection of the free parameters of density estimates in general so that the expected error is within a given constant multiple of the best possible error. The paradigm can be used in nearly all density estimates and for most model selection problems, both parametric and nonparametric. It is the first book on this topic. The text is intended for...
Density estimation has evolved enormously since the days of bar plots and histograms, but researchers and users are still struggling with the problem ...
This book is devoted to Professor Jurgen Lehn, who passed away on September 29, 2008, at the age of 67. It contains invited papers that were presented at the Wo- shop on Recent Developments in Applied Probability and Statistics Dedicated to the Memory of Professor Jurgen Lehn, Middle East Technical University (METU), Ankara, April 23 24, 2009, which was jointly organized by the Technische Univ- sitat Darmstadt (TUD) and METU. The papers present surveys on recent devel- ments in the area of applied probability and statistics. In addition, papers from the Panel Discussion: Impact of Mathematics...
This book is devoted to Professor Jurgen Lehn, who passed away on September 29, 2008, at the age of 67. It contains invited papers that were presented...
Hashing algorithms scramble data and create pseudo-uniform data distribu tions. Bucket algorithms operate on raw untransformed data which are parti tioned Into groups according to membership In equl-slzed d-dlmenslonal hyperrec tangles, called cells or buckets. The bucket data structure Is rather sensitive to the distribution of the data. In these lecture notes, we attempt to explain the connection between the expected time of various bucket algorithms and the dis tribution of the data. The results are Illustrated on standard searching, sorting and selection problems, as well as on a variety...
Hashing algorithms scramble data and create pseudo-uniform data distribu tions. Bucket algorithms operate on raw untransformed data which are parti ti...
Density estimation has evolved enormously since the days of bar plots and histograms, but researchers and users are still struggling with the problem of the selection of the bin widths. This book is the first to explore a new paradigm for the data-based or automatic selection of the free parameters of density estimates in general so that the expected error is within a given constant multiple of the best possible error. The paradigm can be used in nearly all density estimates and for most model selection problems, both parametric and nonparametric.
Density estimation has evolved enormously since the days of bar plots and histograms, but researchers and users are still struggling with the problem ...
A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.
A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervone...
Thls text ls about one small fteld on the crossroads of statlstlcs, operatlons research and computer sclence. Statistleians need random number generators to test and compare estlmators before uslng them ln real l fe. In operatlons research, random numbers are a key component ln arge scale slmulatlons. Computer sclen- tlsts need randomness ln program testlng, game playlng and comparlsons of algo- rlthms. The appl catlons are wlde and varled. Yet all depend upon the same com- puter generated random numbers. Usually, the randomness demanded by an appl catlon has some bullt-ln structure:...
Thls text ls about one small fteld on the crossroads of statlstlcs, operatlons research and computer sclence. Statistleians need random number generat...
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.
Gerard Biau is a professor at Universite Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).
This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now i...
