Special Issue of the ECML PKDD 2018 Journal Track
ISSN:
0885-6125 (Print)
1573-0565 (Online)
In this topical collection (18 articles)
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OriginalPaper
Approximate structure learning for large Bayesian networks
Mauro Scanagatta, Giorgio Corani, Cassio Polpo de Campos… Pages 1209-1227 -
OriginalPaper
Output Fisher embedding regression
Moussab Djerrab, Alexandre Garcia, Maxime Sangnier… Pages 1229-1256 -
OriginalPaper
Global multi-output decision trees for interaction prediction
Konstantinos Pliakos, Pierre Geurts, Celine Vens Pages 1257-1281 -
OriginalPaper
High-dimensional penalty selection via minimum description length principle
Kohei Miyaguchi, Kenji Yamanishi Pages 1283-1302 -
OriginalPaper
Stagewise learning for noisy k-ary preferences
Yuangang Pan, Bo Han, Ivor W. Tsang Pages 1333-1361 -
OriginalPaper
Deep Gaussian Process autoencoders for novelty detection
Rémi Domingues, Pietro Michiardi, Jihane Zouaoui, Maurizio Filippone Pages 1363-1383 -
OriginalPaper
A new method of moments for latent variable models
Matteo Ruffini, Marta Casanellas, Ricard Gavaldà Pages 1431-1455 -
OriginalPaper
Similarity encoding for learning with dirty categorical variables
Patricio Cerda, Gaël Varoquaux, Balázs Kégl Pages 1477-1494 -
OriginalPaper
ML-Plan: Automated machine learning via hierarchical planning
Felix Mohr, Marcel Wever, Eyke Hüllermeier Pages 1495-1515 -
OriginalPaper
Inverse reinforcement learning from summary data
Antti Kangasrääsiö, Samuel Kaski Pages 1517-1535 -
OriginalPaper
Learning from binary labels with instance-dependent noise
Aditya Krishna Menon, Brendan van Rooyen, Nagarajan Natarajan Pages 1561-1595 -
OriginalPaper
Optimizing non-decomposable measures with deep networks
Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla… Pages 1597-1620 -
OriginalPaper
Local contrast as an effective means to robust clustering against varying densities
Bo Chen, Kai Ming Ting, Takashi Washio, Ye Zhu Pages 1621-1645
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