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ORCIDMachine Learning, Volume 108
Volume 108, Number 1, January 2019
- Daniel Berrar
, Philippe Lopes, Jesse Davis, Werner Dubitzky:
Guest editorial: special issue on machine learning for soccer. 1-7 - Werner Dubitzky, Philippe Lopes, Jesse Davis, Daniel Berrar:
The Open International Soccer Database for machine learning. 9-28 - Ondrej Hubácek, Gustav Sourek, Filip Zelezný:
Learning to predict soccer results from relational data with gradient boosted trees. 29-47 - Anthony C. Constantinou:
Dolores: a model that predicts football match outcomes from all over the world. 49-75 - Alkeos Tsokos, Santhosh Narayanan, Ioannis Kosmidis, Gianluca Baio, Mihai Cucuringu, Gavin Whitaker, Franz J. Király:
Modeling outcomes of soccer matches. 77-95 - Daniel Berrar, Philippe Lopes, Werner Dubitzky:
Incorporating domain knowledge in machine learning for soccer outcome prediction. 97-126 - Ulf Brefeld, Jan Lasek, Sebastian Mair:
Probabilistic movement models and zones of control. 127-147
Volume 108, Number 2, February 2019
- Ioannis Tsamardinos, Giorgos Borboudakis, Pavlos Katsogridakis, Polyvios Pratikakis, Vassilis Christophides:
A greedy feature selection algorithm for Big Data of high dimensionality. 149-202 - Muhammad Farooq, Ingo Steinwart:
Learning rates for kernel-based expectile regression. 203-227 - Marc Boullé, Clément Charnay, Nicolas Lachiche:
A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data. 229-266 - Han-Jia Ye, De-Chuan Zhan, Yuan Jiang:
Fast generalization rates for distance metric learning. 267-295 - Martin Mozina, Janez Demsar, Ivan Bratko, Jure Zabkar:
Extreme value correction: a method for correcting optimistic estimations in rule learning. 297-329 - Kai Ming Ting, Ye Zhu, Mark J. Carman, Yue Zhu, Takashi Washio, Zhi-Hua Zhou:
Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms. 331-376 - Alkeos Tsokos, Santhosh Narayanan, Ioannis Kosmidis, Gianluca Baio, Mihai Cucuringu, Gavin Whitaker, Franz J. Király:
Correction to: Modeling outcomes of soccer matches. 377-378
Volume 108, Number 3, March 2019
- Alexander Gammerman, Vladimir Vovk, Henrik Boström, Lars Carlsson:
Conformal and probabilistic prediction with applications: editorial. 379-380 - Vladimir Vapnik, Rauf Izmailov:
Rethinking statistical learning theory: learning using statistical invariants. 381-423 - Vladimir V'yugin, Vladimir G. Trunov:
Online aggregation of unbounded losses using shifting experts with confidence. 425-444 - Vladimir Vovk, Jieli Shen, Valery Manokhin, Min-ge Xie:
Nonparametric predictive distributions based on conformal prediction. 445-474 - Giovanni Cherubin:
Majority vote ensembles of conformal predictors. 475-488 - Paolo Toccaceli, Alexander Gammerman:
Combination of inductive mondrian conformal predictors. 489-510 - Charalambos Eliades, Ladislav Lenc, Pavel Král, Harris Papadopoulos:
Automatic face recognition with well-calibrated confidence measures. 511-534 - Ulf Johansson, Tuve Löfström, Henrik Linusson, Henrik Boström:
Efficient Venn predictors using random forests. 535-550
Volume 108, Number 4, April 2019
- Antonio Vergari, Nicola Di Mauro, Floriana Esposito:
Visualizing and understanding Sum-Product Networks. 551-573 - Kshiteej Sheth, Dinesh Garg, Anirban Dasgupta:
Improved linear embeddings via Lagrange duality. 575-594 - Xenophon Evangelopoulos, Austin J. Brockmeier, Tingting Mu, John Yannis Goulermas:
Continuation methods for approximate large scale object sequencing. 595-626 - Aditya Krishna Menon:
The risk of trivial solutions in bipartite top ranking. 627-658 - Ronghua Shang, Yang Meng, Chiyang Liu, Licheng Jiao, Amir Masoud Ghalamzan Esfahani, Rustam Stolkin:
Unsupervised feature selection based on kernel fisher discriminant analysis and regression learning. 659-686 - Sayash Kapoor, Kumar Kshitij Patel, Purushottam Kar:
Corruption-tolerant bandit learning. 687-715
Volume 108, Number 5, May 2019
- Masashi Sugiyama, Yung-Kyun Noh:
Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018). 717-719 - Hideaki Kano, Junya Honda, Kentaro Sakamaki, Kentaro Matsuura, Atsuyoshi Nakamura, Masashi Sugiyama:
Good arm identification via bandit feedback. 721-745 - Ming Huang, Fuzhen Zhuang, Xiao Zhang, Xiang Ao, Zhengyu Niu, Min-Ling Zhang, Qing He:
Supervised representation learning for multi-label classification. 747-763 - Kanghoon Lee, Geon-hyeong Kim, Pedro A. Ortega, Daniel D. Lee, Kee-Eung Kim:
Bayesian optimistic Kullback-Leibler exploration. 765-783 - Yu-Lin Tsou, Hsuan-Tien Lin:
Annotation cost-sensitive active learning by tree sampling. 785-807 - Joey Tianyi Zhou, Ivor W. Tsang, Shen-Shyang Ho, Klaus-Robert Müller:
N-ary decomposition for multi-class classification. 809-830 - Bo Han, Quanming Yao, Yuangang Pan, Ivor W. Tsang, Xiaokui Xiao, Qiang Yang, Masashi Sugiyama:
Millionaire: a hint-guided approach for crowdsourcing. 831-858 - Jingchang Liu, Linli Xu, Shuheng Shen, Qing Ling:
An accelerated variance reducing stochastic method with Douglas-Rachford splitting. 859-878
Volume 108, Number 6, June 2019
- Ralf Eggeling, Ivo Grosse, Mikko Koivisto:
Algorithms for learning parsimonious context trees. 879-911 - Vítor Cerqueira, Luís Torgo, Fábio Pinto, Carlos Soares:
Arbitrage of forecasting experts. 913-944 - Onur Atan, William R. Zame, Qiaojun Feng, Mihaela van der Schaar:
Constructing effective personalized policies using counterfactual inference from biased data sets with many features. 945-970 - Gérard Biau, Benoît Cadre, Laurent Rouvìère:
Accelerated gradient boosting. 971-992 - He Yan, Qiaolin Ye, Dong-Jun Yu:
Efficient and robust TWSVM classification via a minimum L1-norm distance metric criterion. 993-1018 - Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou:
A simple homotopy proximal mapping algorithm for compressive sensing. 1019-1056
Volume 108, Number 7, July 2019
- Nicolas Lachiche, Christel Vrain, Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese:
Preface to special issue on Inductive Logic Programming, ILP 2017 and 2018. 1057-1059 - Guest editors' note. 1061
- Andrew Cropper, Stephen H. Muggleton:
Learning efficient logic programs. 1063-1083 - Evangelos Michelioudakis, Alexander Artikis, Georgios Paliouras:
Semi-supervised online structure learning for composite event recognition. 1085-1110 - Arnaud Nguembang Fadja, Fabrizio Riguzzi:
Lifted discriminative learning of probabilistic logic programs. 1111-1135 - Pascal Welke, Tamás Horváth, Stefan Wrobel:
Probabilistic and exact frequent subtree mining in graphs beyond forests. 1137-1164 - Victor Guimarães, Aline Paes, Gerson Zaverucha:
Online probabilistic theory revision from examples with ProPPR. 1165-1189
Volume 108, Numbers 8-9, September 2019
- Karsten M. Borgwardt, Po-Ling Loh, Evimaria Terzi, Antti Ukkonen:
Introduction to the special issue for the ECML PKDD 2019 journal track. 1191-1192 - Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin:
Dynamic principal projection for cost-sensitive online multi-label classification. 1193-1230 - Dmitry Adamskiy, Anthony Bellotti, Raisa Dzhamtyrova, Yuri Kalnishkan:
Aggregating Algorithm for prediction of packs. 1231-1260 - Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown:
Efficient feature selection using shrinkage estimators. 1261-1286 - Astrid Dahl, Edwin V. Bonilla:
Grouped Gaussian processes for solar power prediction. 1287-1306 - Benjamin Cowen, Apoorva Nandini Saridena, Anna Choromanska:
LSALSA: accelerated source separation via learned sparse coding. 1307-1327 - Rohit Babbar, Bernhard Schölkopf:
Data scarcity, robustness and extreme multi-label classification. 1329-1351 - Paul Prasse, René Knaebel, Lukás Machlica, Tomás Pevný, Tobias Scheffer:
Joint detection of malicious domains and infected clients. 1353-1368 - Archit Sharma, Siddhartha Saxena, Piyush Rai:
A flexible probabilistic framework for large-margin mixture of experts. 1369-1393 - Ragunathan Mariappan, Vaibhav Rajan:
Deep collective matrix factorization for augmented multi-view learning. 1395-1420 - Shun-Yao Shih, Fan-Keng Sun, Hung-Yi Lee:
Temporal pattern attention for multivariate time series forecasting. 1421-1441 - Joni Pajarinen, Hong Linh Thai, Riad Akrour, Jan Peters, Gerhard Neumann:
Compatible natural gradient policy search. 1443-1466 - Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan:
TD-regularized actor-critic methods. 1467-1501 - Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin:
On PAC-Bayesian bounds for random forests. 1503-1522 - Matthew J. Holland, Kazushi Ikeda:
Efficient learning with robust gradient descent. 1523-1560 - Tom J. Viering, Jesse H. Krijthe, Marco Loog:
Nuclear discrepancy for single-shot batch active learning. 1561-1599 - Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber:
Improving latent variable descriptiveness by modelling rather than ad-hoc factors. 1601-1611 - Cyrus Cousins, Matteo Riondato:
CaDET: interpretable parametric conditional density estimation with decision trees and forests. 1613-1634 - Ievgen Redko, Amaury Habrard, Marc Sebban:
On the analysis of adaptability in multi-source domain adaptation. 1635-1652 - Jan Arne Telle, José Hernández-Orallo, Cèsar Ferri:
The teaching size: computable teachers and learners for universal languages. 1653-1675 - Balázs Csanád Csáji, Krisztián Balázs Kis:
Distribution-free uncertainty quantification for kernel methods by gradient perturbations. 1677-1699 - Zhize Li, Tianyi Zhang, Shuyu Cheng, Jun Zhu, Jian Li:
Stochastic gradient Hamiltonian Monte Carlo with variance reduction for Bayesian inference. 1701-1727
Volume 108, Number 10, October 2019
- Lionel Tabourier, Daniel Faria Bernardes, Anne-Sophie Libert, Renaud Lambiotte:
RankMerging: a supervised learning-to-rank framework to predict links in large social networks. 1729-1756 - Kien Do, Truyen Tran, Thin Nguyen, Svetha Venkatesh:
Attentional multilabel learning over graphs: a message passing approach. 1757-1781 - Bamdev Mishra, Hiroyuki Kasai, Pratik Jawanpuria, Atul Saroop:
A Riemannian gossip approach to subspace learning on Grassmann manifold. 1783-1803 - Xiangju Qin, Paul Blomstedt, Eemeli Leppäaho, Pekka Parviainen, Samuel Kaski:
Distributed Bayesian matrix factorization with limited communication. 1805-1830 - Asso Hamzehei, Raymond K. Wong, Danai Koutra, Fang Chen:
Collaborative topic regression for predicting topic-based social influence. 1831-1850 - Lemei Zhang, Peng Liu, Jon Atle Gulla:
Dynamic attention-integrated neural network for session-based news recommendation. 1851-1875 - Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfahringer, Geoff Holmes, Talel Abdessalem:
Correction to: Adaptive random forests for evolving data stream classification. 1877-1878
Volume 108, Number 11, November 2019
- Ehsan Sadrfaridpour, Talayeh Razzaghi, Ilya Safro:
Engineering fast multilevel support vector machines. 1879-1917 - Alexander Luedtke, Emilie Kaufmann, Antoine Chambaz:
Asymptotically optimal algorithms for budgeted multiple play bandits. 1919-1949 - Aleksandr Y. Aravkin, Giulio Bottegal, Gianluigi Pillonetto:
Boosting as a kernel-based method. 1951-1974 - Wataru Kumagai, Takafumi Kanamori:
Risk bound of transfer learning using parametric feature mapping and its application to sparse coding. 1975-2008 - Aida Brankovic, Luigi Piroddi:
A distributed feature selection scheme with partial information sharing. 2009-2034
Volume 108, Number 12, December 2019
- Di Ma, Songcan Chen:
2D compressed learning: support matrix machine with bilinear random projections. 2035-2060 - Kshitij Khare, Sang-Yun Oh, Syed Rahman, Bala Rajaratnam:
A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data. 2061-2086 - Eric Bax, Lingjie Weng, Xu Tian:
Speculate-correct error bounds for k-nearest neighbor classifiers. 2087-2111 - Gregor H. W. Gebhardt, Andras Gabor Kupcsik, Gerhard Neumann:
The kernel Kalman rule - Efficient nonparametric inference by recursive least-squares and subspace projections. 2113-2157 - Qidi Peng, Nan Rao, Ran Zhao:
Covariance-based dissimilarity measures applied to clustering wide-sense stationary ergodic processes. 2159-2195
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