Special Issue on Conformal and Probabilistic Prediction with Applications
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
Conformal prediction is a framework for complementing the predictions of machine learning algorithms with reliable measures of their accuracy. It has been used in combination with many popular techniques, including support vector machines and neural networks, and has been successfully applied to many challenging real world problems. The framework has been extended to additional problem settings, such as semi-supervised learning, anomaly detection, feature selection, and active learning. Recent developments in collecting large volumes of data have also required its adjustment to handle "big data".
This special issue follows the Seventh Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2018) held in June 2018 at Maastricht University, The Netherlands. However, authors are also invited to submit previously unpublished manuscripts and papers published in proceedings of other conferences, not only COPA 2018, provided the topic is relevant to conformal and probabilistic prediction.
There will be usual restrictions for papers that, at the time of submission, have appeared in archived conference proceedings (such as COPA 2018 papers, published in the Proceedings of Machine Learning Research, volume 91). Such a paper will be considered for publication in this special issue provided the submission contains at least 40% of new material compared with the conference version of the paper. The authors of such submissions will be required to enclose a letter detailing the differences from the conference version.
The list of possible topics includes, but is not limited to:
Learning by conformal predictors
Theoretical analysis of performance guarantees for conformal prediction and other methods of machine learning
Applications of conformal prediction in various fields, including bioinformatics, medicine and pharmaceutical industry
Machine learning in information security and other applications of machine learning
Novel non-conformity measures
Conformal anomaly detection
Learning with Venn predictors and other methods of multiprobability prediction
Conformal predictive distributions
Machine learning with on-line compression modelling