Monotonicity Hints
暂无分享,去创建一个
A hint is any piece of side information about the target function to be learned. We consider the monotonicity hint, which states that the function to be learned is monotonic in some or all of the input variables. The application of monotonicity hints is demonstrated on two real-world problems- a credit card application task, and a problem in medical diagnosis. A measure of the monotonicity error of a candidate function is defined and an objective function for the enforcement of monotonicity is derived from Bayesian principles. We report experimental results which show that using monotonicity hints leads to a statistically significant improvement in performance on both problems.
[1] Yaser S. Abu-Mostafa,et al. Learning from Hints , 1994, J. Complex..
[2] Yaser S. Abu-Mostafa,et al. Learning from hints in neural networks , 1990, J. Complex..
[3] Yaser S. Abu-Mostafa,et al. Hints and the VC Dimension , 1993, Neural Computation.
[4] Yann LeCun,et al. Efficient Pattern Recognition Using a New Transformation Distance , 1992, NIPS.
[5] A. Refenes. Neural Networks in the Capital Markets , 1994 .