Luc Devroye's Books

A Probabilistic Theory of Pattern Recognition

By: Luc Devroye,Laszlo Györfi,Gabor Lugosi

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A Probabilistic Theory of Pattern Recognition

By: Luc Devroye,Laszlo Györfi,Gabor Lugosi

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.

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631

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1461207118

ISBN 13

9781461207115

Lectures on the Nearest Neighbor Method

By: Gérard Biau,Luc Devroye

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Lectures on the Nearest Neighbor Method

By: Gérard Biau,Luc Devroye

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).

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Page Count

284

Publisher

Springer

Published Date

ISBN 10

3319253883

ISBN 13

9783319253886

Combinatorial Methods in Density Estimation

By: Luc Devroye,Gabor Lugosi

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Combinatorial Methods in Density Estimation

By: Luc Devroye,Gabor Lugosi

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.

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Page Count

219

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1461301254

ISBN 13

9781461301257

Non-Uniform Random Variate Generation

By: Luc Devroye

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Non-Uniform Random Variate Generation

By: Luc Devroye

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: typlcally, one needs more than just a sequence of Independent random blts or Independent uniform 0,1] random vari ables. Some users need random variables wlth unusual densltles, or random com blnatorlal objects wlth speclftc propertles, or random geometrlc objects, or ran dom processes wlth weil deftned dependence structures. Thls ls preclsely the sub ject area of the book, the study of non-uniform random varlates. The plot evolves around the expected complexlty of random varlate genera tlon algorlthms. We set up an ldeal zed computatlonal model (wlthout overdolng lt), we lntroduce the notlon of unlformly bounded expected complexlty, and we study upper and lower bounds for computatlonal complexlty. In short, a touch of computer sclence ls added to the fteld. To keep everythlng abstract, no tlmlngs or computer programs are lncluded. Thls was a Iabor of Iove. George Marsagl a created CS690, a course on ran dom number generat on at the School of Computer Sclence of McG ll Unlverslty.

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Page Count

859

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1461386438

ISBN 13

9781461386438

Bayesian Inference in Dynamic Econometric Models

By: Luc Bauwens,Michel Lubrano,Jean-François Richard

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Bayesian Inference in Dynamic Econometric Models

By: Luc Bauwens,Michel Lubrano,Jean-François Richard

This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.

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Page Count

370

Publisher

OUP Oxford

Published Date

ISBN 10

0191588466

ISBN 13

9780191588464

Machine Learning: ECML 2001

12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001. Proceedings

By: Luc de Raedt,Peter Flach

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Machine Learning: ECML 2001

12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001. Proceedings

By: Luc de Raedt,Peter Flach

This book constitutes the refereed proceedings of the 12th European Conference on Machine Learning, ECML 2001, held in Freiburg, Germany, in September 2001. The 50 revised full papers presented together with four invited contributions were carefully reviewed and selected from a total of 140 submissions. Among the topics covered are classifier systems, naive-Bayes classification, rule learning, decision tree-based classification, Web mining, equation discovery, inductive logic programming, text categorization, agent learning, backpropagation, reinforcement learning, sequence prediction, sequential decisions, classification learning, sampling, and semi-supervised learning.

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Page Count

635

Publisher

Springer Science & Business Media

Published Date

ISBN 10

3540425365

ISBN 13

9783540425366

Lectures on the Nearest Neighbor Method

By: Gérard Biau,Luc Devroye

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Lectures on the Nearest Neighbor Method

By: Gérard Biau,Luc Devroye

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Page Count

284

Publisher

Springer

Published Date

ISBN 10

3319253883

ISBN 13

9783319253886

Combinatorial Methods in Density Estimation

By: Luc Devroye,Gabor Lugosi

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Combinatorial Methods in Density Estimation

By: Luc Devroye,Gabor Lugosi

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Page Count

219

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Springer Science & Business Media

Published Date

ISBN 10

1461301254

ISBN 13

9781461301257

A Probabilistic Theory of Pattern Recognition

By: Luc Devroye,Laszlo Györfi,Gabor Lugosi

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A Probabilistic Theory of Pattern Recognition

By: Luc Devroye,Laszlo Györfi,Gabor Lugosi

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Solve jigsaw puzzle

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Page Count

631

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1461207118

ISBN 13

9781461207115

Book's by Luc Devroye

A Probabilistic Theory of Pattern Recognition

A Probabilistic Theory of Pattern Recognition

Lectures on the Nearest Neighbor Method

Lectures on the Nearest Neighbor Method

Combinatorial Methods in Density Estimation

Combinatorial Methods in Density Estimation

Non-Uniform Random Variate Generation

Non-Uniform Random Variate Generation

Bayesian Inference in Dynamic Econometric Models

Bayesian Inference in Dynamic Econometric Models

Machine Learning: ECML 2001

Machine Learning: ECML 2001

Lectures on the Nearest Neighbor Method

Lectures on the Nearest Neighbor Method

Combinatorial Methods in Density Estimation

Combinatorial Methods in Density Estimation

A Probabilistic Theory of Pattern Recognition

A Probabilistic Theory of Pattern Recognition

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