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Extreme Value Theory-Based Methods for Visual Recognition, Walter J. Scheirer


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Автор: Walter J. Scheirer
Название:  Extreme Value Theory-Based Methods for Visual Recognition
Перевод названия: Уолтер Шайрер: Теоретически обоснованные методы предельных значений для визуального распознавания
ISBN: 9781627057004
Издательство: Turpin
Классификация:

ISBN-10: 1627057005
Обложка/Формат: Paperback
Страницы: 131
Вес: 0.24 кг.
Дата издания: 28.02.2017
Серия: Synthesis lectures on computer vision
Язык: English
Размер: 235 x 191 x 7
Читательская аудитория: General (us: trade)
Ключевые слова: Artificial intelligence,Computer vision, COMPUTERS / Computer Vision & Pattern Recognition,COMPUTERS / Intelligence (AI) & Semantics
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Поставляется из: Англии
Описание: A common feature of many approaches to modeling sensory statistics is an emphasis on capturing the average. From early representations in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian distribution are a seemingly natural and obvious choice for modeling sensory data. However, insights from neuroscience, psychology, and computer vision suggest an alternate strategy: preferentially focusing representational resources on the extremes of the distribution of sensory inputs. The notion of treating extrema near a decision boundary as features is not necessarily new, but a comprehensive statistical theory of recognition based on extrema is only now just emerging in the computer vision literature. This book begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. In contrast to central-tendency modeling, it is hypothesized that distributions near decision boundaries form a more powerful model for recognition tasks by focusing coding resources on data that are arguably the most diagnostic features. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. The second part of the book uses the theory to describe a new class of machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes methods for post-recognition score analysis, information fusion, multi-attribute spaces, and calibration of supervised machine learning algorithms.


Data Analysis Using Stata, Third Edition

Автор: Kohler
Название: Data Analysis Using Stata, Third Edition
ISBN: 1597181102 ISBN-13(EAN): 9781597181105
Издательство: Taylor&Francis
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Цена: 11176.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание:

Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks.

The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand.

Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.

Matrix Methods in Data Mining and Pattern Recognition

Автор: Lars Eld?n
Название: Matrix Methods in Data Mining and Pattern Recognition
ISBN: 0898716268 ISBN-13(EAN): 9780898716269
Издательство: Cambridge Academ
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Цена: 9029.00 р.
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Описание: Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed include classification of handwritten digits, text mining, text summarization, pagerank computations related to the Google search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.

Extreme Events in Finance - A Handbook of Extreme Value Theory and its Applications

Автор: Francois Longin
Название: Extreme Events in Finance - A Handbook of Extreme Value Theory and its Applications
ISBN: 1118650190 ISBN-13(EAN): 9781118650196
Издательство: Wiley
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Цена: 20742.00 р.
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Описание: "Extreme Events in Finance: A Handbook of Extreme Value Theory and its Applications features a combination of the theory, methods, and applications of extreme value theory (EVT) in finance as well as a practical understanding of market behavior including both ordinary and extraordinary conditions"--

Finite element methods for Navier-Stokes equations : theory and algorithms

Автор: Vivette Girault; Pierre-Arnaud Raviart
Название: Finite element methods for Navier-Stokes equations : theory and algorithms
ISBN: 3642648886 ISBN-13(EAN): 9783642648885
Издательство: Springer
Цена: 11179.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: The material covered by this book has been taught by one of the authors in a post-graduate course on Numerical Analysis at the University Pierre et Marie Curie of Paris. It is an extended version of a previous text (cf. Girault & Raviart [32J) published in 1979 by Springer-Verlag in its series: Lecture Notes in Mathematics.

In the last decade, many engineers and mathematicians have concentrated their efforts on the finite element solution of the Navier-Stokes equations for incompressible flows. The purpose of this book is to provide a fairly comprehen- sive treatment of the most recent developments in that field. To stay within reasonable bounds, we have restricted ourselves to the case of stationary prob- lems although the time-dependent problems are of fundamental importance.

This topic is currently evolving rapidly and we feel that it deserves to be covered by another specialized monograph. We have tried, to the best of our ability, to present a fairly exhaustive treatment of the finite element methods for inner flows. On the other hand however, we have entirely left out the subject of exterior problems which involve radically different techniques, both from a theoretical and from a practical point of view.

Also, we have neither discussed the implemen- tation of the finite element methods presented by this book, nor given any explicit numerical result. This field is extensively covered by Peyret & Taylor [64J and Thomasset [82].

Energy Minimization Methods in Computer Vision and Pattern Recognition

Автор: Alan L. Yuille; Song-Chun Zhu; Daniel Cremers; Yon
Название: Energy Minimization Methods in Computer Vision and Pattern Recognition
ISBN: 354074195X ISBN-13(EAN): 9783540741954
Издательство: Springer
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Цена: 14673.00 р.
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Описание: Constitutes the proceedings of the 6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2007, held in Ezhou, China in August 2007. This work contains papers organized in topical sections on algorithms, applications, image parsing, image processing, shape and three-dimensional processing.

Energy Minimization Methods in Computer Vision and Pattern Recognition

Автор: Daniel Cremers; Yuri Boykov; Andrew Blake; Frank R
Название: Energy Minimization Methods in Computer Vision and Pattern Recognition
ISBN: 3642036406 ISBN-13(EAN): 9783642036408
Издательство: Springer
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Цена: 13974.00 р.
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Описание: 7th International Conference EMMCVPR 2009 Bonn Germany August 2427 2009 Proceedings. .


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