  In this paper, a progressive learning technique for multi-class
classification is proposed. This newly developed learning technique is
independent of the number of class constraints and it can learn new classes
while still retaining the knowledge of previous classes. Whenever a new class
(non-native to the knowledge learnt thus far) is encountered, the neural
network structure gets remodeled automatically by facilitating new neurons and
interconnections, and the parameters are calculated in such a way that it
retains the knowledge learnt thus far. This technique is suitable for
real-world applications where the number of classes is often unknown and online
learning from real-time data is required. The consistency and the complexity of
the progressive learning technique are analyzed. Several standard datasets are
used to evaluate the performance of the developed technique. A comparative
study shows that the developed technique is superior.
