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Titlebook: Machine Learning for Audio, Image and Video Analysis; Theory and Applicati Francesco Camastra,Alessandro Vinciarelli Textbook 20081st editi

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发表于 2025-3-21 18:49:29 | 显示全部楼层 |阅读模式
书目名称Machine Learning for Audio, Image and Video Analysis
副标题Theory and Applicati
编辑Francesco Camastra,Alessandro Vinciarelli
视频video
概述Provides detailed introductions to algorithms and examples of their applications.Domains that appear far from one another such as speech and handwriting recognition are shown to be equivalent from the
丛书名称Advanced Information and Knowledge Processing
图书封面Titlebook: Machine Learning for Audio, Image and Video Analysis; Theory and Applicati Francesco Camastra,Alessandro Vinciarelli Textbook 20081st editi
描述1. 1 TwoFundamentalQuestions There are two fundamental questions that should be answered before buying, and even more before reading, a book: • Why should one read the book? • What is the book about? This is the reason why this section, the ?rst of the whole text, proposes some motivations for potential readers (Section 1. 1. 1) and an overall description of the content (Section 1. 1. 2). If the answers are convincing, further information can be found in the rest of this chapter: Section 1. 2 shows in detail the str- ture of the book, Section 1. 3 presents some features that can help the reader to better move through the text, and Section 1. 4 provides some reading tracks targeting speci?c topics. 1. 1. 1 Why Should One Read The Book? One of the most interesting technological phenomena in recent years is the di?usion of consumer electronic products with constantly increasing acqui- tion, storage and processing power. As an example, consider the evolution of digital cameras: the ?rst models available in the market in the early nineties produced images composed of 1. 6 million pixels (this is the meaning of the expression 1. 6 megapixels), carried an onboard memory of 16 megabytes, a
出版日期Textbook 20081st edition
关键词Classification; Clustering; Ensemble methods; Face verification; HSV; Hidden Markov methods; Kernel method
版次1
doihttps://doi.org/10.1007/978-1-84800-007-0
issn_series 1610-3947
copyrightSpringer-Verlag London 2008
The information of publication is updating

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发表于 2025-3-21 20:22:50 | 显示全部楼层
Audio Acquisition, Representation and Storageh as phones, radio and television, videogames, CD players, cellular phones, etc. However, although there is a wide spectrum of applications, the main problems to be addressed in order to manipulate digital sound are essentially three: acquisition, representation and storage. The acquisition is the p
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Kernel Methods was introduced by [1] in 1964. Two decades later several authors [60] [68] [70] proposed a neural network, . (RBF), based on the kernel functions which was widely used in many applicative fields. Since 1995 kernel methods have conquered a fundamental place in machine learning when . (SVMs) were pro
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Speech and Handwriting Recognitione of the most widely investigated applications of the literature, but also to show how the same machine learning techniques can be applied to recognize data apparently different like handwritten word images and speech recordings. In fact, the only differences between handwriting and speech recogniti
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