Growing Adaptive Machines, Taras Kowaliw; Nicolas Bredeche; Ren? Doursat
Название: Growing adaptive machines ISBN: 3642553362 ISBN-13(EAN): 9783642553363 Издательство: Springer Рейтинг: Цена: 17074.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The particular focus is on how to design artificial neural networks for engineering tasks.The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research.
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 Рейтинг: Цена: 17097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Yunqian Ma; Guodong Guo Название: Support Vector Machines Applications ISBN: 3319022997 ISBN-13(EAN): 9783319022994 Издательство: Springer Рейтинг: Цена: 19377.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Murty Название: Support Vector Machines and Perceptrons ISBN: 3319410628 ISBN-13(EAN): 9783319410623 Издательство: Springer Рейтинг: Цена: 6097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.>
In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining.
Examples of topics which have developed from the advances of ICA, which are covered in the book are:
A unifying probabilistic model for PCA and ICA
Optimization methods for matrix decompositions
Insights into the FastICA algorithm
Unsupervised deep learning
Machine vision and image retrieval
A review of developments in the theory and applications of independent component analysis, and its influence in important areas such as statistical signal processing, pattern recognition and deep learning
A diverse set of application fields, ranging from machine vision to science policy data
Contributions from leading researchers in the field
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