Learning Bias in Neural Networks and an Approach to Controlling Its Effect in Monotonic Classification

Authors:
N. P. Archer;S. Wang
Affiliations:
-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1993

Citing 3
Cited 1

Machine learning of inductive bias

Machine learning of inductive bias
Probabilistic neural networks

Neural Networks
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations

Monotone and partially monotone neural networks

IEEE Transactions on Neural Networks

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Abstract

As a learning machine, a neural network using the backpropagation training algorithm is subject to learning bias. This results in unpredictability of boundary generation behavior in pattern recognition applications, especially in the case of small training sample size. It is suggested that in a large class of pattern recognition problems such as managerial and other problems possessing monotonicity properties, the effect of learning bias can be controlled by using multiarchitecture monotonic function neural networks.