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ORCIDMachine Learning, Volume 107
Volume 107, Number 1, January 2018
- Pavel Brazdil
, Christophe G. Giraud-Carrier:
Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue. 1-14 - Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Efficient benchmarking of algorithm configurators via model-based surrogates. 15-41 - Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme:
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization. 43-78 - Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren:
Speeding up algorithm selection using average ranking and active testing by introducing runtime. 79-108 - Mario A. Muñoz, Laura Villanova, Davaatseren Baatar, Kate Smith-Miles:
Instance spaces for machine learning classification. 109-147 - Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
The online performance estimation framework: heterogeneous ensemble learning for data streams. 149-176 - Pavel Kordík, Ján Cerný, Tomás Frýda:
Discovering predictive ensembles for transfer learning and meta-learning. 177-207 - Ana Carolina Lorena, Aron I. Maciel, Péricles B. C. de Miranda, Ivan G. Costa, Ricardo B. C. Prudêncio:
Data complexity meta-features for regression problems. 209-246 - Brandon M. Malone, Kustaa Kangas, Matti Järvisalo, Mikko Koivisto, Petri Myllymäki:
Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction. 247-283 - Iván Olier, Noureddin Sadawi, G. Richard J. Bickerton, Joaquin Vanschoren, Crina Grosan, Larisa N. Soldatova, Ross D. King:
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. 285-311
Volume 107, Number 2, February 2018
- Subhadeep Mukhopadhyay, Shinjini Nandi:
LPiTrack: Eye movement pattern recognition algorithm and application to biometric identification. 313-331 - Vojtech Franc, Ondrej Fikar, Karel Bartos, Michal Sofka:
Learning data discretization via convex optimization. 333-355 - Konstantinos Sechidis, Gavin Brown:
Simple strategies for semi-supervised feature selection. 357-395 - Christopher R. Stephens, Hugo Flores Huerta, Ana Ruiz Linares:
When is the Naive Bayes approximation not so naive? 397-441 - Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker:
Emotion in reinforcement learning agents and robots: a survey. 443-480
Volume 107, Number 3, March 2018
- Alhussein Fawzi, Omar Fawzi, Pascal Frossard:
Analysis of classifiers' robustness to adversarial perturbations. 481-508 - Simone Romano, Xuan Vinh Nguyen, Karin Verspoor, James Bailey:
The randomized information coefficient: assessing dependencies in noisy data. 509-549 - Sen Su, Yakun Wang, Zhongbao Zhang, Cheng Chang, Muhammad Azam Zia:
Identifying and tracking topic-level influencers in the microblog streams. 551-578 - Vincent Cottet, Pierre Alquier:
1-Bit matrix completion: PAC-Bayesian analysis of a variational approximation. 579-603 - Colin Bellinger, Christopher Drummond, Nathalie Japkowicz:
Manifold-based synthetic oversampling with manifold conformance estimation. 605-637
Volume 107, Number 4, April 2018
- Wee Sun Lee, Robert J. Durrant:
Foreword: special issue for the journal track of the 9th Asian Conference on Machine Learning (ACML 2017). 639-641 - Tomohiko Mizutani, Mirai Tanaka:
Efficient preconditioning for noisy separable nonnegative matrix factorization problems by successive projection based low-rank approximations. 643-673 - Bo Han, Yuangang Pan, Ivor W. Tsang:
Robust Plackett-Luce model for k-ary crowdsourced preferences. 675-702 - Tong Wei, Lan-Zhe Guo, Yufeng Li, Wei Gao:
Learning safe multi-label prediction for weakly labeled data. 703-725 - Chi Zhang, Peilin Zhao, Shuji Hao, Yeng Chai Soh, Bu-Sung Lee, Chunyan Miao, Steven C. H. Hoi:
Distributed multi-task classification: a decentralized online learning approach. 727-747 - Yao-Xiang Ding, Zhi-Hua Zhou:
Crowdsourcing with unsure option. 749-766 - Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Semi-supervised AUC optimization based on positive-unlabeled learning. 767-794 - Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning. 795
Volume 107, Number 5, May 2018
- Manali Sharma, Mustafa Bilgic:
Learning with rationales for document classification. 797-824 - Haimonti Dutta, Ashwin Srinivasan:
Consensus-based modeling using distributed feature construction with ILP. 825-858 - Sophie Burkhardt, Stefan Kramer:
Online multi-label dependency topic models for text classification. 859-886 - Pierre Alquier, Benjamin Guedj:
Simpler PAC-Bayesian bounds for hostile data. 887-902 - Dirk Schäfer, Eyke Hüllermeier:
Dyad ranking using Plackett-Luce models based on joint feature representations. 903-941
Volume 107, Number 6, June 2018
- Xuan Peng, Xunzhang Gao, Xiang Li:
On better training the infinite restricted Boltzmann machines. 943-968 - Ajin George Joseph, Shalabh Bhatnagar:
An incremental off-policy search in a model-free Markov decision process using a single sample path. 969-1011 - Enric Junqué de Fortuny, David Martens, Foster J. Provost:
Wallenius Bayes. 1013-1037 - Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead A. Williamson, Perry Groot, Tom Heskes:
A scalable preference model for autonomous decision-making. 1039-1068 - Omid Keivani, Kaushik Sinha, Parikshit Ram:
Improved maximum inner product search with better theoretical guarantee using randomized partition trees. 1069-1094
Volume 107, Number 7, July 2018
- James Cussens, Alessandra Russo:
Preface to the special issue on inductive logic programming. 1095-1096 - Stephen H. Muggleton, Wang-Zhou Dai, Claude Sammut, Alireza Tamaddoni-Nezhad, Jing Wen, Zhi-Hua Zhou:
Meta-Interpretive Learning from noisy images. 1097-1118 - Stephen H. Muggleton, Ute Schmid, Christina Zeller, Alireza Tamaddoni-Nezhad, Tarek R. Besold:
Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP. 1119-1140 - Peter Schüller, Mishal Benz:
Best-effort inductive logic programming via fine-grained cost-based hypothesis generation - The inspire system at the inductive logic programming competition. 1141-1169 - Michael Bain, Ashwin Srinivasan:
Identification of biological transition systems using meta-interpreted logic programs. 1171-1206
Volume 107, Numbers 8-10, September 2018
- Jesse Davis, Björn Bringmann, Élisa Fromont, Derek Greene:
Guest editors introduction to the special issue for the ECML PKDD 2018 journal track. 1207-1208 - Mauro Scanagatta, Giorgio Corani, Cassio Polpo de Campos, Marco Zaffalon:
Approximate structure learning for large Bayesian networks. 1209-1227 - Moussab Djerrab, Alexandre Garcia, Maxime Sangnier, Florence d'Alché-Buc:
Output Fisher embedding regression. 1229-1256 - Konstantinos Pliakos, Pierre Geurts, Celine Vens:
Global multi-output decision trees for interaction prediction. 1257-1281 - Kohei Miyaguchi, Kenji Yamanishi:
High-dimensional penalty selection via minimum description length principle. 1283-1302 - François Petitjean, Wray L. Buntine, Geoffrey I. Webb, Nayyar Abbas Zaidi:
Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processes. 1303-1331 - Yuangang Pan, Bo Han, Ivor W. Tsang:
Stagewise learning for noisy k-ary preferences. 1333-1361 - Remi Domingues, Pietro Michiardi, Jihane Zouaoui, Maurizio Filippone:
Deep Gaussian Process autoencoders for novelty detection. 1363-1383 - Ajin George Joseph, Shalabh Bhatnagar:
An online prediction algorithm for reinforcement learning with linear function approximation using cross entropy method. 1385-1429 - Matteo Ruffini, Marta Casanellas, Ricard Gavaldà:
A new method of moments for latent variable models. 1431-1455 - Wenjie Zheng, Aurélien Bellet, Patrick Gallinari:
A distributed Frank-Wolfe framework for learning low-rank matrices with the trace norm. 1457-1475 - Patricio Cerda, Gaël Varoquaux, Balázs Kégl:
Similarity encoding for learning with dirty categorical variables. 1477-1494 - Felix Mohr, Marcel Wever, Eyke Hüllermeier:
ML-Plan: Automated machine learning via hierarchical planning. 1495-1515 - Antti Kangasrääsiö, Samuel Kaski:
Inverse reinforcement learning from summary data. 1517-1535 - Vitalik Melnikov, Eyke Hüllermeier:
On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. 1537-1560 - Aditya Krishna Menon, Brendan van Rooyen, Nagarajan Natarajan:
Learning from binary labels with instance-dependent noise. 1561-1595 - Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla, Fabrizio Sebastiani:
Optimizing non-decomposable measures with deep networks. 1597-1620 - Bo Chen, Kai Ming Ting, Takashi Washio, Ye Zhu:
Local contrast as an effective means to robust clustering against varying densities. 1621-1645
Volume 107, Number 11, November 2018
- Michelangelo Ceci, Toon Calders:
Introduction to the special issue on discovery science. 1647-1649 - Juan Gabriel Colonna, João Gama, Eduardo Freire Nakamura:
A comparison of hierarchical multi-output recognition approaches for anuran classification. 1651-1671 - Martin Breskvar, Dragi Kocev, Saso Dzeroski:
Ensembles for multi-target regression with random output selections. 1673-1709 - Ali Pesaranghader, Herna L. Viktor, Eric Paquet:
Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams. 1711-1743 - Fabíola Souza F. Pereira, João Gama, Sandra de Amo, Gina M. B. Oliveira:
On analyzing user preference dynamics with temporal social networks. 1745-1773 - Cláudio Rebelo de Sá, Wouter Duivesteijn, Paulo J. Azevedo, Alípio Mário Jorge, Carlos Soares, Arno J. Knobbe:
Discovering a taste for the unusual: exceptional models for preference mining. 1775-1807 - Matej Mihelcic, Tomislav Smuc:
Targeted and contextual redescription set exploration. 1809-1846 - Pascal Welke, Tamás Horváth, Stefan Wrobel:
Probabilistic frequent subtrees for efficient graph classification and retrieval. 1847-1873 - Timo Nolle, Stefan Luettgen, Alexander Seeliger, Max Mühlhäuser:
Analyzing business process anomalies using autoencoders. 1875-1893
Volume 107, Number 12, December 2018
- Ioannis Tsamardinos, Elissavet Greasidou, Giorgos Borboudakis:
Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation. 1895-1922 - Rémi Flamary, Marco Cuturi, Nicolas Courty, Alain Rakotomamonjy:
Wasserstein discriminant analysis. 1923-1945 - Thi Nhat Anh Nguyen, Abdesselam Bouzerdoum, Son Lam Phung:
Stochastic variational hierarchical mixture of sparse Gaussian processes for regression. 1947-1986 - Shounak Datta, Supritam Bhattacharjee, Swagatam Das:
Clustering with missing features: a penalized dissimilarity measure based approach. 1987-2025 - Sumanta Singha, Prakash P. Shenoy:
An adaptive heuristic for feature selection based on complementarity. 2027-2071
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