Then, a sample of learning resources that came with 100 commonly used textbooks in the disciplines of logistics/supply chain management and information systems were classified using the framework.
To see a sampling of learning apps for tablets and smartphones, go here.
The starting point is the conjecture, proved in specific cases, that image representations which are invariant to translation, scaling and other transformations can considerably reduce the sample complexity of learning.
Nevertheless, the results of the integration agent for our JSL image database are comparable to the results for ViHASi image database, suggesting that our approach requires only a small amount of samples for learning.
This analysis, involving all samples and not making use of learning samples, does not resolve whether or not interbreeding occurs between NSH and ELW.
Next, to avoid selection bias from a pattern of selection of learning samples, we repeat the entire process 20 times by shuffling samples at every 10-fold CV.
Standardized tests indirectly assess only a small sample of any area of learning.
Boosting is a method of combining ensemble classifiers created from a weighted version of learning sample, where weights have been adjusted at each step to provide increased weight to cases misclassified earlier.
Any realistic model of learning from samples must address the issue of noisy data.
There are three main approaches with emphasis on reducing training samples and improving the efficiency of learning machines.
