NOTE: The following materials are presented for timely
dissemination of academic and technical work. Copyright and all other rights
therein are reserved by authors and/or other copyright holders. Persoanl
use of the following materials is permitted and, however, people using
the materials or information are expected to adhere to the terms and
constraints invoked by the related copyright.
Combining Linear Discriminant Functions with Neural Networks for Supervised
Learning
ABSTRACT
A novel supervised learning method is presented by combining linear
discriminant functions with neural networks. The proposed method results
in a tree-structured hybrid architecture. Due to constructive learning,
the binary tree hierarchical architecture is automatically generated by
a controlled growing process for a specific supervised learning task.
Unlike the classic decision tree, the linear discriminant functions are
merely employed in the intermediate level of the tree for heuristically
partitioning a large and complicated task into several smaller and simpler
subtasks in the proposed method. These subtasks are dealt with by component
neural networks at the leaves of the tree accordingly. For constructive
learning, growing and credit-assignment algorithms are developed to serve for
the hybrid architecture. The proposed architecture provides an efficient
way to apply existing neural networks (e.g. multi-layered perceptron) for
solving a large scale problem. We have already applied the proposed method
to a universal approximation problem and several benchmark classification
problems in order to evaluate its performance. Simulation results have shown
that the proposed method yields better results and faster training in
comparison with the multi-layered perceptron.
Click
nca97.pdf
for full text