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Наименование:

Application to Compression of Digital Images Support Vector Machine Learning.

Автор:   Jonathan R.
мягкая обложка
176 страниц
2008 год
Издательство:   Книга по Требованию
Цена:
2781 руб
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Описание:It is demonstrated that compression is more efficient in frequency space. In the frequency domain, results are superior to that of JPEG. Several novel algorithms are developed applying SVM learning to both directly model the colour surface and model transform coefficients after the surface has been transformed into the frequency domain. It is this property that is exploited as the basis for image compression algorithms presented in this book. Methods exploring the application of support vector machine learning (SVM) to still image compression are detailed in both the spatial and frequency domains. In particular the sparse properties of SVM learning are exploited in the compression algorithms. For example, the quality of the industry standard ‘Lena’ image compressed 63: 1 for JPEG is slightly worse quality than the same image compressed 192: 1 with the RKi-1 algorithm detailed in this book. An SVM has the property that it will choose the minimum number of training points to use as centres of the Gaussian kernel functions. A classic radial basis function neural network requires that the topology of the network be defined before training.


Orthomol Immun Junior Сила иммунитета на страже Вашего ребенка Orthomol Immun Junior Сила иммунитета на страже Вашего ребенка

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Essays

Richard Kelsey gave this as an idea whose time has come again in the last panel, and I completely agree with him. My guess (and Microsoft's guess, it seems) is that much computing will move from the desktop onto remote servers. In other words, time-sharing is back. And I think there will need to be support for it at the language level. For example, I know that Richard and Jonathan Rees have done a lot of work implementing process scheduling within Scheme 48. 3. Efficiency. Recently it was starting to seem that computers were finally fast enough. More and more we were starting to hear about byte code, which implies to me at least that we feel we have cycles to spare. But I don't think we will, with server-based software. Someone is going to have to pay for the servers that the software runs on, and the number of users they can support per machine will be the divisor of their capital cost. So I think efficiency will matter, at least in computational bottlenecks. It will be especially important to do i/o fast, because server-based applications do a lot of i/o ...»

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