COPA 2012 - 1st Conformal Prediction and its Applications Workshop(COPA 2012)

Workshop Program Committee (not complete)
Vineeth Balasubramanian, Arizona State University, USA
Anthony Bellotti, Imperial College London, UK
David R. Hardoon, SAS, Singapore
Shen-Shyang Ho, Nanyang Technological University, Singapore
Zakria Hussain, University College London, UK
Yuri Kalnishkan, Royal Holloway, University of London, UK
Matjaz Kukar, University of Ljubljana, Slovenia
Antonis Lambrou, Royal Holloway, University of London, UK
Rikard Laxhammar, University of Skovde, Sweden
Yang Li, Chinese Academy of Sciences, China
Zhiyuan Luo, Royal Holloway, University of London, UK
Andrea Murari, Consorzio RFX, Italy
Ilia Nouretdinov, Royal Holloway, University of London, UK
Savvas Pericleous, Frederick University, Cyprus
David Surkov, Egham Capital, UK
Jesus Vega, Asociacion EURATOM/CIEMAT para Fusion, Spain
Fan Yang, Xiamen University, China
Workshop Aim: Quantifying the uncertainty of the predictions produced by classification and regression techniques is an important problem in the field of Machine Learning. Conformal Prediction is a recently developed framework for complementing the predictions of Machine Learning algorithms with reliable measures of confidence. The methods developed based on this framework produce well-calibrated confidence measures for individual examples without assuming anything more than that the data are generated independently by the same probability distribution (i.i.d.). Since its development the framework has been combined with many popular techniques, such as Support Vector Machines, k-Nearest Neighbours, Neural Networks, Ridge Regression etc., and has been successfully applied to many challenging real world problems, such as the early detection of ovarian cancer, the classification of leukaemia subtypes, the diagnosis of acute abdominal pain, the assessment of stroke risk, the recognition of hypoxia in electroencephalograms (EEGs), the prediction of plant promoters, the prediction of network traffic demand, the estimation of effort for software projects and the backcalculation of non-linear pavement layer moduli. The framework has also been extended to additional problem settings such as feature selection, outlier detection, change detection in streams and active learning. The aim of this workshop 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 Prediction and its applications.