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This vector would contain the brightness values for each pixel in each band in each training class. The mean, standard deviation, variance-covariance matrix, and correlation matrix are calculated from the measurement vectors. Once the statistics from each training site are determined, the most effective bands for each class should be selected.
Nonasymptotic theory of the rate of convergence of learning processes How fast is the rate of convergence of the learning process? Theory of controlling the generalization ability of learning processes How can one control the rate of convergence (the generalization ability) of the learning process? Theory of constructing learning machines
A class is called a universal Glivenko–Cantelli class if it is a GC class with respect to any probability measure on (,). A class is a weak uniform Glivenko–Cantelli class if the convergence occurs uniformly over all probability measures P {\displaystyle \mathbb {P} } on ( S , A ) {\displaystyle ({\mathcal {S}},A)} : For every ε > 0 ...
A diagram showing a perceptron updating its linear boundary as more training examples are added Below is an example of a learning algorithm for a single-layer perceptron with a single output unit. For a single-layer perceptron with multiple output units, since the weights of one output unit are completely separate from all the others', the same ...
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It is known that in the dual space of any Montel space, a sequence converges in the strong dual topology if and only if it converges in the weak* topology, [25] which in particular, is the reason why a sequence of distributions converges (in the strong dual topology) if and only if it converges pointwise (this leads many authors to use ...
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