Question

the accumulated data from various sources is processed in one shot it is called?​

Answer 1

Answer:

One-shot learning is an object categorization problem, found mostly in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of samples/images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training samples/images.

The primary focus of this article will be on the solution to this problem presented by Fei-Fei Li, R. Fergus and P. Perona in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 28(4), 2006, which uses a generative object category model and variational Bayesian framework for representation and learning of visual object categories from a handful of training examples. Another paper, presented at the International Conference on Computer Vision and Pattern Recognition (CVPR) 2000 by Erik Miller, Nicholas Matsakis, and Paul Viola will also be discussed.