Fetching the paper…
Reading the bibliography…
Assessing the performance of a learned model is a crucial part of machine learning.
An introduction to the bootstrap
B Efron and RJ Tibshirani · 1994
Earlier work this paper cites.
The use of the area under the ROC curve in the evaluation of machine learning algorithms
AP Bradley · 1997
Earlier work this paper cites.
Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
JA Blackard and DJ Dean · 1999
Earlier work this paper cites.
Enhancing supervised learning with unlabeled data
SA Goldman and Y Zhou · 2000
Earlier work this paper cites.
Text classification from labeled and unlabeled documents using EM
K Nigam, AK McCallum, S Thrun, and T Mitchell · 2000
Earlier work this paper cites.
Asymptotic Statistics
AW Van der Vaart · 2000
Earlier work this paper cites.
Estimating the support of a high-dimensional distribution
B Schölkopf, JC Platt, J Shawe-Taylor, AJ Smola, and RC Williamson · 2001
Earlier work this paper cites.
Building text classifiers using positive and unlabeled examples
B Liu, Y Dai, X Li, WS Lee, and PS Yu · 2003
Earlier work this paper cites.
PEBL: Web page classification without negative examples
H Yu, J Han, and KC-C Chang · 2004
Cited alongside, same era.
Learning from positive and unlabeled examples
F Denis, R Gilleron, and F Letouzey · 2005
Cited alongside, same era.
Learning from labeled and unlabeled data: An empirical study across techniques and domains
NV Chawla and GI Karakoulas · 2005
Cited alongside, same era.
Semi-supervised learning
O Chapelle, B Schölkopf, A Zien, et al · 2006
Cited alongside, same era.
The relationship between Precision-Recall and ROC curves
J Davis and M Goadrich · 2006
Cited alongside, same era.
Learning Bayesian classifiers from positive and unlabeled examples
B Calvo, P Larrañaga, and JA Lozano · 2007
Cited alongside, same era.
Novelty detection: Unlabeled data definitely help
C Scott and G Blanchard · 2009
Later among the works it cites.
Deep transfer via second-order Markov logic
J Davis and P Domingos · 2009
Later among the works it cites.
ProDiGe: Prioritization of disease genes with multitask machine learning from positive and unlabeled examples
F Mordelet and J-P Vert · 2011
Later among the works it cites.
eXtasy: variant prioritization by genomic data fusion
A Sifrim, D Popovic, L-C Tranchevent, A Ardeshirdavani, R Sakai, P Konings, JR Vermeesch, J Aerts, B De Moor, and Y Moreau · 2013
Later among the works it cites.
A bagging SVM to learn from positive and unlabeled examples
F Mordelet and J-P Vert · 2014
Later among the works it cites.
Statistical hypothesis testing in positive unlabelled data
K Sechidis, B Calvo, and G Brown · 2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning classifiers from only positive and unlabeled data
C Elkan and K Noto · 2008
Cited alongside, same era.
Optimization techniques for semi-supervised support vector machines
O Chapelle, V Sindhwani, and SS Keerthi · 2008
Cited alongside, same era.
A robust ensemble approach to learn from positive and unlabeled data using SVM base models
M Claesen, F De Smet, J Suykens, and B De Moor · 2015
Closest in time.
Marc Claesen, Frank De Smet, Pieter Gillard, Chantal Mathieu, and Bart De Moor · 2015
Closest in time.