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Machine Learning, Marsland



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Цена: 9486р.
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Ориентировочная дата поставки: конец Сентября- начало Октября
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Автор: Marsland
Название:  Machine Learning
ISBN: 9781466583283
Издательство: Taylor&Francis
Классификация:
ISBN-10: 1466583282
Обложка/Формат: Mixed media product
Страницы: 457
Вес: 1.006 кг.
Дата издания: 17.11.2014
Серия: Computing & IT
Язык: English
Издание: 2 revised edition
Иллюстрации: 21 tables, black and white; 205 illustrations, black and white
Размер: 262 x 183 x 24
Читательская аудитория: College/higher education
Ключевые слова: Computer science, COMPUTERS / General,COMPUTERS / Databases / Data Mining,COMPUTERS / Programming / Algorithms
Основная тема: Machine Learning - Design
Подзаголовок: An algorithmic perspective, second edition
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Англии



      Старое издание
Machine Learning

Автор: Stephen Marsland
Название: Machine Learning
ISBN: 1420067184 ISBN-13(EAN): 9781420067187
Издательство: Taylor&Francis
Цена: 7149 р.
Наличие на складе: Невозможна поставка.
Описание: Covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. This book includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material.


Machine Learning

Автор: Kevin Murphy
Название: Machine Learning
ISBN: 0262018020 ISBN-13(EAN): 9780262018029
Издательство: MIT Press
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Цена: 12052 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание:

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Pattern Recognition and Machine Learning

Автор: Christopher M. Bishop
Название: Pattern Recognition and Machine Learning
ISBN: 0387310738 ISBN-13(EAN): 9780387310732
Издательство: Springer
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Цена: 9816 р.
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Описание: The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.A forthcoming companion volume will deal with practical aspects of pattern recognition and machine learning, and will include free software implementations of the key algorithms along with example data sets and demonstration programs.Christopher Bishop is Assistant Director at Microsoft Research Cambridge, and also holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, and was recently elected Fellow of the Royal Academy of Engineering. The author's previous textbook "Neural Networks for Pattern Recognition" has been widely adopted.Coming soon:*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)*For instructors, worked solutions to remaining exercises from the Springer web site*Lecture slides to accompany each chapter*Data sets available for download

Bayesian Reasoning and Machine Learning

Автор: Barber
Название: Bayesian Reasoning and Machine Learning
ISBN: 0521518148 ISBN-13(EAN): 9780521518147
Издательство: Cambridge Academ
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Цена: 8353 р.
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Описание: Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

Practical Machine Learning with H2O

Автор: Darren Cook
Название: Practical Machine Learning with H2O
ISBN: 149196460X ISBN-13(EAN): 9781491964606
Издательство: Wiley
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Цена: 5499 р.
Наличие на складе: Есть (1 шт.)
Описание: This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.

Machine Learning

Автор: Mitchell
Название: Machine Learning
ISBN: 0071154671 ISBN-13(EAN): 9780071154673
Издательство: McGraw-Hill
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Цена: 8386 р.
Наличие на складе: Поставка под заказ.

Описание: Covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

Statistics, Data Mining, and Machine Learning in Astronomy

Название: Statistics, Data Mining, and Machine Learning in Astronomy
ISBN: 0691151687 ISBN-13(EAN): 9780691151687
Издательство: Wiley
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Цена: 11275 р.
Наличие на складе: Поставка под заказ.

Описание: Provides an introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope.

Machine Learning in Image Steganalysis

Автор: Schaathun
Название: Machine Learning in Image Steganalysis
ISBN: 0470663057 ISBN-13(EAN): 9780470663059
Издательство: Wiley
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Цена: 11516 р.
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Описание: Steganography is the art of communicating a secret message, hiding the very existence of a secret message. This book is an introduction to steganalysis as part of the wider trend of multimedia forensics, as well as a practical tutorial on machine learning in this context.

Reinforcement and Systemic Machine Learning for Decision Making

Автор: Kulkarni
Название: Reinforcement and Systemic Machine Learning for Decision Making
ISBN: 047091999X ISBN-13(EAN): 9780470919996
Издательство: Wiley
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Цена: 13750 р.
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Описание: * Authors have both industrial and academic experiences * Case studies are included reflecting author`s industrial experiences * Downloadable tutorials are available .

First Course in Machine Learning

Автор: Rogers Simon
Название: First Course in Machine Learning
ISBN: 1439824142 ISBN-13(EAN): 9781439824146
Издательство: Taylor&Francis
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Цена: 5499 р.
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Combinatorial Machine Learning

Автор: Moshkov
Название: Combinatorial Machine Learning
ISBN: 3642209947 ISBN-13(EAN): 9783642209949
Издательство: Springer
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Цена: 15014 р.
Наличие на складе: Поставка под заказ.

Описание: Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation. Reducts play key role in the problem of attribute(feature) selection. The aims of this book are (i) the consideration of the setsof decision trees, rules and reducts; (ii) study of relationships among theseobjects; (iii) design of algorithms for construction of trees, rules and reducts;and (iv) obtaining bounds on their complexity. Applications for supervisedmachine learning, discrete optimization, analysis of acyclic programs, faultdiagnosis, and pattern recognition are considered also. This is a mixture ofresearch monograph and lecture notes. It contains many unpublished results.However, proofs are carefully selected to be understandable for students.The results considered in this book can be useful for researchers in machinelearning, data mining and knowledge discovery, especially for those who areworking in rough set theory, test theory and logical analysis of data. The bookcan be used in the creation of courses for graduate students.

Ensemble Machine Learning

Автор: Zhang
Название: Ensemble Machine Learning
ISBN: 1441993258 ISBN-13(EAN): 9781441993250
Издательство: Springer
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Цена: 21588 р.
Наличие на складе: Поставка под заказ.

Описание: It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

Machine Learning for Computer Vision

Автор: Cipolla
Название: Machine Learning for Computer Vision
ISBN: 3642286607 ISBN-13(EAN): 9783642286605
Издательство: Springer
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Цена: 16191 р.
Наличие на складе: Поставка под заказ.

Описание: Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. A summary of the past Computer Vision Summer Schools can be found at: http://www.dmi.unict.it/icvss This edited volume contains a selection of articles covering some of the talks and tutorials held during the last editions of the school. The chapters provide an in-depth overview of challenging areas with key references to the existing literature.


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