  Lifelong machine learning (LML) is an area of machine learning research
concerned with human-like persistent and cumulative nature of learning. LML
system's objective is consolidating new information into an existing machine
learning model without catastrophically disrupting the prior information. Our
research addresses this LML retention problem for creating a knowledge
consolidation network through task rehearsal without retaining the prior task's
training examples. We discovered that the training data reconstruction error
from a trained Restricted Boltzmann Machine can be successfully used to
generate accurate virtual examples from the reconstructed set of a uniform
random set of examples given to the trained model. We also defined a measure
for comparing the probability distributions of two datasets given to a trained
network model based on their reconstruction mean square errors.
