In this chapter, we describe how the p-values derived from the conformal predictions framework can be used for active learning; that is, to select the informative examples from a data collection that can be used to train a classifier for best performance. We show the connection of this approach to information-theoretic methods, as well as show how the methodology can be generalized to multiple classifier models and information fusion settings.
|Original language||English (US)|
|Title of host publication||Conformal Prediction for Reliable Machine Learning|
|Subtitle of host publication||Theory, Adaptations and Applications|
|Number of pages||22|
|State||Published - Apr 2014|
All Science Journal Classification (ASJC) codes
- Computer Science(all)