  Humans can learn concepts or recognize items from just a handful of examples,
while machines require many more samples to perform the same task. In this
paper, we build a computational model to investigate the possibility of this
kind of rapid learning. The proposed method aims to improve the learning task
of input from sensory memory by leveraging the information retrieved from
long-term memory. We present a simple and intuitive technique called cognitive
discriminative mappings (CDM) to explore the cognitive problem. First, CDM
separates and clusters the data instances retrieved from long-term memory into
distinct classes with a discrimination method in working memory when a sensory
input triggers the algorithm. CDM then maps each sensory data instance to be as
close as possible to the median point of the data group with the same class.
The experimental results demonstrate that the CDM approach is effective for
learning the discriminative features of supervised classifications with few
training sensory input instances.
