Soft computing machine learning (239 Ergebnisse)

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Zustand: Brand New. New.SoftCover International edition. Different ISBN and Cover image but contents are same as US edition.Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

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Paperback. Zustand: Good. None Issued. Some pencil notes and underlining found scattered throughout the text; previous owner's signature has been covered up with white-out on the first free end page. Binding is sturdy. Covers show some light wear around the corners and at the head and base of the spine. International/Priority shipping at cost. 614pp.…

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paperback. Zustand: Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned.…

Paperback. Zustand: Brand New. 88 pages. 9.00x6.00x0.25 inches. In Stock.

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Image Quality Assessment is well-known for measuring the perceived image degradation of natural scene images but is still an emerging topic for computer-generated images. This book addresses this problem and presents recent advances based on soft computing. It is aimed at students, practitioners and researchers in the field of image processing and related areas such as computer graphics and visualization. In this book, we first clarify the differences between natural scene images and computer-generated images, and address the problem of Image Quality Assessment (IQA) by focusing on the visual perception of noise. Rather than using known perceptual models, we first investigate the use of soft computing approaches, classically used in Artificial Intelligence, as full-reference and reduced-reference metrics. Thus, by creating Learning Machines, such as SVMs and RVMs, we can assess the perceptual quality of a computer-generated image. We also investigate the use of interval-valuedfuzzy sets as a no-reference metric. These approaches are treated both theoretically and practically, for the complete process of IQA. The learning step is performed using a database built from experiments with human users and the resulting models can be used for any image computed with a stochastic rendering algorithm. This can be useful for detecting the visual convergence of the different parts of an image during the rendering process, and thus to optimize the computation. These models can also be extended to other applications that handle complex models, in the fields of signal processing and image processing.…