  Conventional theoretical machine learning studies generally assume explicitly
or implicitly that there are enough or even infinitely supplied computational
resources. In real practice, however, computational resources are usually
limited, and the performance of machine learning depends not only on how many
data have been received, but also on how many data can be handled subject to
computational resources available. Note that most current ``intelligent
supercomputing'' facilities work like exclusive operating systems, where a
fixed amount of resources are allocated to a machine learning task without
adaptive scheduling strategies considering important factors such as the
learning performance demands and learning process status. In this article, we
introduce the notion of machine learning throughput, define Computational
Resource Efficient Learning (CoRE-Learning), and present a theoretical
framework that takes into account the influence of computational resources in
learning theory. This framework can be naturally applied to stream learning
where the incoming data streams can be potentially endless with overwhelming
size and it is impractical to assume that all received data can be handled in
time. It may also provide a theoretical perspective for the design of
intelligent supercomputing operating systems.
