منابع مشابه
High Dimensional Wavelet Smoothing
A fundamental issue in Data Mining is the development of algorithms to extract some useful information from very large databases. One important technique is to estimate a smooth surface approximating the data. However, the number of observations can be of the order of millions and there may be hundreds of variables recorded so one has to deal with the so-called ”curse of dimensionality”. The al...
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Wavelets can be thought of as a set of well-localized basis functions with very good approximation properties. The diiculty in applying wavelet approximation to high-dimensional data is that the number of basis functions increases exponentially with the number of dimensions, making the application of standard mathematical methods for determining coeecients diic ult. We propose a modeling method...
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A fundamental issue in Data Mining is the development of algorithms to extract useful information from very large databases. One important technique is to estimate a smooth function approximating the data. Such an approximation can for example be used for visualisation, prediction, or classification purposes. However, the number of observations can be of the order of millions and there may be h...
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Images from functional imaging experiments are subject to a data processing stream involving motion correction and spatial normalization. The next step is then to smooth the images using a Gaussian kernel. One reason for the smoothing step is to render activation information amenable to classical statistical inference via Random Field Theory. This requires that the residual fields have a smooth...
متن کاملWavelet Smoothing for Data with Autocorrelated Errors
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ژورنال
عنوان ژورنال: ANZIAM Journal
سال: 2000
ISSN: 1445-8810
DOI: 10.21914/anziamj.v42i0.634