Conformal prediction was developed originally at the end of the 1990s and summarized in the monograph “Algorithmic Learning in a Random World”, Springer, New York, 2005. The main purpose of this method is to complement predictions delivered by various algorithms of Machine Learning with provably valid measures of their accuracy and reliability under the assumption that the observations are independent and identically distributed.
Conformal prediction is a universal tool in several senses; in particular, it can be used in combination with any known machine learning algorithm, such as SVM, Neural Networks, Ridge Regression, etc. It has been applied to a variety of problems from diagnostics of depression to the behaviour of bots.
A sister method of Venn prediction was developed at the same time as conformal prediction and is used for probabilistic prediction. The COPA series of workshops/symposia is a home for work in both conformal and Venn prediction, as reflected in its full name “Conformal and Probabilistic Prediction with Applications”. The aim of this symposium is to serve as a forum for the presentation of new and ongoing work and the exchange of ideas between researchers on any aspect of Conformal and Probabilistic Prediction and their applications to interesting problems of any field.
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User Name : cressida
Posted 23-11-2017 on 16:13:23 AEDT