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Abstract:
Monotonicity is a constraint which arises in many application
domains. We present a machine learning model called the monotonic
network, which has the ability to obey monotonicity constraints
exactly, i.e., by virtue of functional form. A straightforward
method for implementing and training a monotonic network is
described. Monotonic networks are proven to be universal
approximators of continous, differentiable monotonic functions. We
apply monotonic networks to a real-world task in corporate bond
rating prediction and show that they compare favorably to other
approaches.
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