Sufletul Pereche Te Cheama Pdf Download
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of the forward error probability matrix gives the eigenvalues, or
weights, as well as the eigenvectors, or principal components, of
the probability matrix. The
eigenvalues determine the
magnitude of the
eigenvectors (the position of the peaks of the principal components),
and the eigenvectors determine the location of the
magnitude of the principal components. The eigenvectors that are
negative in magnitude will be used to obtain the final classifier.
Note that the principal components are in increasing order of
magnitude.”
I gave you an example of the PCA process, but there are two significant changes I would make to the example I gave.
I would not use a "word" vector in my example. I would use a "number" vector, such as 0 through 99.
When I say that the principal components are in increasing order of magnitude, I mean that the first principal component has the largest magnitude.
The first change is that using the words 0 through 99 I have used in the example makes no sense. This is because the training samples are supposed to be words. However, you don't want to map the 0 to the zero vector, you want to map 0 to a word.
The second change is that using principal components that have negative magnitude is no longer correct. When you do principal component analysis, you are trying to reduce the word frequency matrix to the number of dimensions that are most important for representing the features. As the dimensions become more important, the less important dimensions become less important. For example, the first principal
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