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Support Vector Machines Applications, Yunqian Ma; Guodong Guo


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Автор: Yunqian Ma; Guodong Guo
Название:  Support Vector Machines Applications
ISBN: 9783319022994
Издательство: Springer
Классификация:
ISBN-10: 3319022997
Обложка/Формат: Hardcover
Страницы: 302
Вес: 0.58 кг.
Дата издания: 03.03.2014
Язык: English
Издание: 2014 ed.
Иллюстрации: 37 tables, black and white; 56 illustrations, color; 31 illustrations, black and white; vii, 302 p. 87 illus., 56 illus. in color.
Размер: 240 x 162 x 17
Читательская аудитория: Professional & vocational
Основная тема: Signal, Image and Speech Processing
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Offering a detailed, unified approach, this book examines advances and applications of Support Vector Machines: image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, artificial intelligence and more.


Advanced Machine Learning Technologies and Applications

Автор: Aboul Ella Hassanien; Mohamed Tolba; Ahmad Taher A
Название: Advanced Machine Learning Technologies and Applications
ISBN: 3319134604 ISBN-13(EAN): 9783319134604
Издательство: Springer
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Цена: 11460.00 р.
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Описание: This book constitutes the refereed proceedings of the Second International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2014, held in Cairo, Egypt, in November 2014. machine learning in watermarking/authentication and virtual machines;

Support Vector Machines and Perceptrons

Автор: Murty
Название: Support Vector Machines and Perceptrons
ISBN: 3319410628 ISBN-13(EAN): 9783319410623
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>

Twin Support Vector Machines

Автор: Jayadeva; Reshma Khemchandani; Suresh Chandra
Название: Twin Support Vector Machines
ISBN: 3319461842 ISBN-13(EAN): 9783319461847
Издательство: Springer
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Цена: 19564.00 р.
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Описание: This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.

Least Squares Support Vector Machin

Название: Least Squares Support Vector Machin
ISBN: 9812381511 ISBN-13(EAN): 9789812381514
Издательство: World Scientific Publishing
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Цена: 15840.00 р.
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Описание: An examination of least squares support vector machines (LS-SVMs) which are reformulations to standard SVMs. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is also discussed.

Artificial Intelligence for Marketing: Practical Applications

Автор: Sterne Jim
Название: Artificial Intelligence for Marketing: Practical Applications
ISBN: 1119406331 ISBN-13(EAN): 9781119406334
Издательство: Wiley
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Цена: 6178.00 р.
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Описание: A straightforward, non-technical guide to the next major marketing tool Artificial Intelligence for Marketing presents a tightly-focused introduction to machine learning, written specifically for marketing professionals.

Knowledge Engineering, Machine Learning and Lattice Computing with Applications

Автор: Manuel Grana; Carlos Toro; Robert J. Howlett; Lakh
Название: Knowledge Engineering, Machine Learning and Lattice Computing with Applications
ISBN: 3642373429 ISBN-13(EAN): 9783642373428
Издательство: Springer
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Цена: 6429.00 р.
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Описание: This book constitutes the refereed proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2012, held in San Sebastian, Spain, in September 2012.

Extreme Learning Machines 2013: Algorithms and Applications

Автор: Fuchen Sun; Kar-Ann Toh; Manuel Grana Romay; Kezhi
Название: Extreme Learning Machines 2013: Algorithms and Applications
ISBN: 331904740X ISBN-13(EAN): 9783319047409
Издательство: Springer
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Цена: 19591.00 р.
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Описание: An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods.

Stochastic Approximation and Recursive Algorithms and Applications

Автор: Harold Kushner; G. George Yin
Название: Stochastic Approximation and Recursive Algorithms and Applications
ISBN: 1441918477 ISBN-13(EAN): 9781441918475
Издательство: Springer
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Цена: 20962.00 р.
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Описание: This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems. This second edition is a thorough revision, although the main features and structure remain unchanged.


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