Multi-target regression algorithms can compensate for a fewer amount of training instances available for a problem by exploiting the knowledge of a similar problem.
Such steganalysis typically uses a certain amount of images for training a classifier: features are extracted from the images and fed to a binary classifier (usually Support Vector Machines) for training.
In the end, despite a valiant push from the younger, upstart squad, the match perhaps came down to experience, a turning point that came 20 minutes before the final bell for which no amount of training can prepare a competitor.
This procedure can effectively exploit weak contextual factors, but it leads to a reduction in the amount of training data available for normal contextual factors.
A method's performance also varies between localization sites and, in general, correlates with the amount of training data available for a given localization.
This is probably due to the small amount of data available for training the classifier and the harsh way of computing the accuracy (the classifier has to predict properly not only two but four distinct classes).
One idea is to gain feedback from users on the perceived quality of the results while simultaneously increasing the amount of data available for training the ranking component, as for our SVM + Pathway model.
Statistical methods are suitable when large amount of data is available for training.
This algorithm needs a large amount of storage and computation for training the object silhouette, which is not suitable for real-time multimedia sensor networks.
These results show that the proposed soft decision tree structure is able to improve the F0 estimation accuracy of the baseline system significantly in small training datasets, and therefore, an important application of the proposed system is in low-resource languages when limited amount of data is available for training.
