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

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書目名稱Machine Learning for Audio, Image and Video Analysis影響因子(影響力)




書目名稱Machine Learning for Audio, Image and Video Analysis影響因子(影響力)學(xué)科排名




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書目名稱Machine Learning for Audio, Image and Video Analysis被引頻次




書目名稱Machine Learning for Audio, Image and Video Analysis被引頻次學(xué)科排名




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書目名稱Machine Learning for Audio, Image and Video Analysis年度引用學(xué)科排名




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FACT

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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BLINK

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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