Information Theory and Statistical Learning, Frank Emmert-Streib; Matthias Dehmer
Автор: Hastie Название: Statistical Learning with Sparsity ISBN: 1498712169 ISBN-13(EAN): 9781498712163 Издательство: Taylor&Francis Рейтинг: Цена: 16843.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Discover New Methods for Dealing with High-Dimensional Data
A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.
Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso.
In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.
Автор: Alexander Gammerman; Vladimir Vovk; Harris Papadop Название: Statistical Learning and Data Sciences ISBN: 3319170902 ISBN-13(EAN): 9783319170909 Издательство: Springer Рейтинг: Цена: 8944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the Third International Symposium on Statistical Learning and Data Sciences, SLDS 2015, held in Egham, Surrey, UK, April 2015.
Автор: Ke-Lin Du; M. N. S. Swamy Название: Neural Networks and Statistical Learning ISBN: 1447170474 ISBN-13(EAN): 9781447170471 Издательство: Springer Рейтинг: Цена: 14365.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Inclusive coverage of all the essential neural network applications in a statistical learning framework makes this a baseline text for students and researchers, with 25 chapters on all the major approaches that include a wealth of examples and exercises.
Автор: Frank Emmert-Streib; Matthias Dehmer Название: Information Theory and Statistical Learning ISBN: 0387848150 ISBN-13(EAN): 9780387848150 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.
Описание: This class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of applications in information security. The book will cover malware detection, cryptography, and intrusion detection. The book will be relevant for students in machine learning and computer security courses.
Автор: Gallager, Robert G. Название: Information theory and reliable communication ISBN: 0471290483 ISBN-13(EAN): 9780471290483 Издательство: Wiley Рейтинг: Цена: 36266.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Explore information theory as it relates to the fundamental aspects of communication systems
Information theory is at work all around us, every day, and in all our communications. Information Theory and Reliable Communication delves into the mathematical models of sources and channels in communication systems and then explores the framework for constructing highly-detailed models of real-world sources and channels. The text then extends further into information theory by breaking encoders and decoders into two parts and studying the mechanisms that make more effective communication systems. Taken as a whole, the book provides exhaustive coverage of the practical use of information theory in developing communications systems.
The two-volume set LNCS 10273 and 10274 constitutes the refereed proceedings of the thematic track on Human Interface and the Management of Information, held as part of the 19th HCI International 2017, in Vancouver, BC, Canada, in July 2017.
HCII 2017 received a total of 4340 submissions, of which 1228 papers were accepted for publication after a careful reviewing process. The 102 papers presented in these volumes were organized in topical sections as follows: Part I: Visualization Methods and Tools; Information and Interaction Design; Knowledge and Service Management; Multimodal and Embodied Interaction. Part II: Information and Learning; Information in Virtual and Augmented Reality; Recommender and Decision Support Systems; Intelligent Systems; Supporting Collaboration and User Communities; Case Studies.
Автор: Robert J. Jannarone Название: Concurrent Learning and Information Processing ISBN: 1461380499 ISBN-13(EAN): 9781461380498 Издательство: Springer Рейтинг: Цена: 14673.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many monitoring, forecasting, and control operations occur in settings where relationships among key measurements must be learned quickly. The book describes benefits and features of the system, statistical foundations for the system, and several related models.
Описание: Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results.
Автор: Jose C. Principe Название: Information Theoretic Learning ISBN: 1461425859 ISBN-13(EAN): 9781461425854 Издательство: Springer Рейтинг: Цена: 21661.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.
Автор: Tie-Yan Liu Название: Learning to Rank for Information Retrieval ISBN: 3642441246 ISBN-13(EAN): 9783642441240 Издательство: Springer Рейтинг: Цена: 13974.00 р. 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: Ratner Название: Statistical & Machine-Learning Data ISBN: 1498797601 ISBN-13(EAN): 9781498797603 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining.
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