Abstract
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) |
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Title of host publication | Conformal Prediction for Reliable Machine Learning |
Subtitle of host publication | Theory, Adaptations and Applications |
Publisher | Elsevier Inc. |
Pages | 49-70 |
Number of pages | 22 |
ISBN (Print) | 9780123985378 |
DOIs | |
State | Published - Apr 2014 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- General Computer Science