Out-of-core learning
Out-of-core learning refers to a set of algorithms working with data that cannot fit into the memory of a single computer, but that can easily fit into some data storage such as a local hard disk or web repository. Your available RAM, the core memory on your single machine, may indeed range from a few gigabytes (sometimes 2 GB, more commonly 4 GB, but we assume that you have 2 GB at maximum) up to 256 GB on large server machines. Large servers are like the ones you can get on cloud computing services such as Amazon Elastic Compute Cloud (EC2), whereas your storage capabilities can easily exceed terabytes of capacity using just an external drive (most likely about 1 TB but it can reach up to 4 TB).
As machine learning is based on globally reducing a cost function, many algorithms initially have been thought to work using all the available data and having access to it at each iteration of the optimization process. This is particularly true for all algorithms based on...
