Low-Rank Approximation, Ivan Markovsky

Автор: Hiroshi Konno; Phan Thien Thach; Hoang Tuy
Название: Optimization on Low Rank Nonconvex Structures
ISBN: 0792343085 ISBN-13(EAN): 9780792343080
Издательство: Springer
Рейтинг:
Цена: 241310.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This work is devoted to global optimization problems with special structures. Most of these problems, though highly nonconvex, can be characterized by the property that they reduce to convex minimization problems when some of the variables are fixed.

Автор: Terras, Audrey
Название: Harmonic analysis on symmetric spaces-higher rank spaces, positive definite matrix space and generalizations
ISBN: 1493934066 ISBN-13(EAN): 9781493934065
Издательство: Springer
Рейтинг:
Цена: 55890.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text is an introduction to harmonic analysis on symmetric spaces, focusing on advanced topics such as higher rank spaces, positive definite matrix space and generalizations.

Автор: Manuela Pavan
Название: Scientific Data Ranking Methods,27
ISBN: 0444530207 ISBN-13(EAN): 9780444530202
Издательство: Elsevier Science
Рейтинг:
Цена: 178540.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Presents basic mathematical aspects of the ranking methods using a didactical approach. This book covers a wide range of applications, from the environment and toxicology to DNA sequencing. It can be applied in several different fields, such as decision support, toxicology, EU priority lists of toxic chemicals, and environmental problems.

Автор: Ivan Markovsky
Название: Low Rank Approximation
ISBN: 1447158369 ISBN-13(EAN): 9781447158363
Издательство: Springer
Рейтинг:
Цена: 104480.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book details the theory, algorithms, and applications of structured low-rank approximation, and presents efficient local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel and Sylvester structured problems and more.

Автор: Hansen
Название: Rank-Deficient and Discrete Ill-Posed Problems
ISBN: 0898714036 ISBN-13(EAN): 9780898714036
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 71060.00 T
Наличие на складе: Нет в наличии.
Описание: Here is an overview of modern computational stabilization methods for linear inversion, with applications to a variety of problems in audio processing, medical imaging, seismology, astronomy, and other areas. Rank-deficient problems involve matrices that are exactly or nearly rank deficient. Such problems often arise in connection with noise suppression and other problems where the goal is to suppress unwanted disturbances of given measurements. Discrete ill-posed problems arise in connection with the numerical treatment of inverse problems, where one typically wants to compute information about interior properties using exterior measurements. Examples of inverse problems are image restoration and tomography, where one needs to improve blurred images or reconstruct pictures from raw data. This book describes new and existing numerical methods for the analysis and solution of rank-deficient and discrete ill-posed problems. The emphasis is on insight into the stabilizing properties of the algorithms and the efficiency and reliability of the computations.

Автор: Yun Fu
Название: Low-Rank and Sparse Modeling for Visual Analysis
ISBN: 3319119990 ISBN-13(EAN): 9783319119991
Издательство: Springer
Рейтинг:
Цена: 102480.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data.

Автор: Yun Fu
Название: Low-Rank and Sparse Modeling for Visual Analysis
ISBN: 3319355678 ISBN-13(EAN): 9783319355672
Издательство: Springer
Рейтинг:
Цена: 79190.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data.

Автор: Tie-Yan Liu
Название: Learning to Rank for Information Retrieval
ISBN: 3642441246 ISBN-13(EAN): 9783642441240
Издательство: Springer
Рейтинг:
Цена: 93160.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The author of this book first reviews the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms. Scientific theoretical soundness is combined with broad development and application experiences.

Автор: Xia Lirong
Название: Learning and Decision-Making from Rank Data
ISBN: 1681734427 ISBN-13(EAN): 9781681734422
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 82230.00 T
Наличие на складе: Невозможна поставка.
Описание: The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.

Автор: Xia Lirong
Название: Learning and Decision-Making from Rank Data
ISBN: 1681734400 ISBN-13(EAN): 9781681734408
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 61910.00 T
Наличие на складе: Невозможна поставка.
Описание: The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.

Автор: Weixun Wang; Prabhat Mishra; Sanjay Ranka
Название: Dynamic Reconfiguration in Real-Time Systems
ISBN: 148999078X ISBN-13(EAN): 9781489990785
Издательство: Springer
Рейтинг:
Цена: 104480.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes the challenges in performing dynamic reconfigurations in real-time systems. It shows how to design efficient support architectures-including dynamic cache reconfiguration, hardware/software partitioning and task mapping and scheduling.