Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)
Joseph Sill
Monotonicity is a constraint which arises in many application do(cid:173) mains. We present a machine learning model, the monotonic net(cid:173) work, for which monotonicity can be enforced exactly, i.e., by virtue offunctional form . A straightforward method for implementing and training a monotonic network is described. Monotonic networks are proven to be universal approximators of continuous, differen(cid:173) tiable monotonic functions. We apply monotonic networks to a real-world task in corporate bond rating prediction and compare them to other approaches.