This permitted a review of classification error: how many training set samples were incorrectly assigned, and classification performance: the range of probabilities observed ahead of deciding the test priorities for scant case material.
The synchronization method has been tested with many different training sets.
Thus, instead of identifying and removing noise explicitly, we employ stochastic sampling with replacement to generate many diverse training sets that are enriched with clean samples, to suppress unwanted noise while allowing diversification to emerge.
Since many candidate training sets need to be evaluated in the course of optimization, we preferred the computationally efficient approximation in Equation (4) over the exact P E V R i d g e (M Test ).
Secondly, assuming the number of seriously noisy samples is far fewer than the number of relatively clean samples, we show that stochastic sampling with replacement can generate many new diverse training sets that are enriched with clean samples.
This typically results in a highly noisy training set, where many training sentences do not express the intended relation.
Manual selections of ∼75 simple spikes and as many complex spikes served as training sets for multilinear discriminant analysis (MLDA), which was used to label all thresholded spikes as either a simple or complex spike on the basis of the spike properties.
When the training sets are small, many objects would not have enough data for training their local models.
Creating training sets automatically from many different sources and discovering new functions through unsupervised clustering of microenvironments improves functional coverage.
The influence produced by over-scaled training set and oversized eigenvector dimension: On the one hand, it can be known from the VCA method that the over-scaled training set, i.e., too many training examples, will result in too many vanishing component polynomials, too many monomials contained in the vanishing component polynomial, and too high order of polynomials.
