Modeling for optimal probability prediction
Wang, Y. & Witten, I.H. (2002). Modeling for optimal probability prediction. In Proceedings of the Nineteenth International Conference on Machine Learning, Sydney, Australia, July (pp. 650-657). San Francisco: Morgan Kaufmann Publishers Inc.
Permanent Research Commons link: https://hdl.handle.net/10289/2131
We present a general modelling method for optimal probability prediction over future observations, in which model dimensionality is determined as a natural by-product. This new method yields several estimators, and we establish theoretically that they are optimal (either overall or under stated restrictions) when the number of free parameters is infinite. As a case study, we investigate the problem of fitting logistic models in finite-sample situations. Simulation results on both artificial and practical datasets are supportive.
Morgan Kaufmann Publishers Inc.
This is the author’s version of a conference paper published in Proceedings of the Nineteenth International Conference on Machine Learning, Sydney, Australia, July. ©2002 Authors