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ORCIDMachine Learning, Volume 106
Volume 106, Number 1, January 2017
- Mohamed Hamza Ibrahim
, Christopher Joseph Pal, Gilles Pesant:
Improving probabilistic inference in graphical models with determinism and cycles. 1-54 - Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Sunil Aryal:
Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors. 55-91 - Vitaly Kuznetsov, Mehryar Mohri:
Generalization bounds for non-stationary mixing processes. 93-117 - Olivier Wintenberger:
Optimal learning with Bernstein online aggregation. 119-141 - Deiner Mena, Elena Montañés, José Ramón Quevedo, Juan José del Coz:
A family of admissible heuristics for A* to perform inference in probabilistic classifier chains. 143-169
Volume 106, Number 2, February 2017
- Ilja Kuzborskij, Francesco Orabona:
Fast rates by transferring from auxiliary hypotheses. 171-195 - Aline Paes, Gerson Zaverucha, Vítor Santos Costa:
On the use of stochastic local search techniques to revise first-order logic theories from examples. 197-241 - Ashwin Srinivasan, Michael Bain:
An empirical study of on-line models for relational data streams. 243-276 - Amol Pande, Liang Li, Jeevanantham Rajeswaran, John Ehrlinger, Udaya B. Kogalur, Eugene Blackstone, Hemant Ishwaran:
Boosted multivariate trees for longitudinal data. 277-305 - Masayuki Karasuyama, Hiroshi Mamitsuka:
Adaptive edge weighting for graph-based learning algorithms. 307-335
Volume 106, Number 3, March 2017
- Jinlong Huang, Qingsheng Zhu, Lijun Yang, Dongdong Cheng, Quanwang Wu:
QCC: a novel clustering algorithm based on Quasi-Cluster Centers. 337-357 - Pedro Ribeiro Mendes Júnior, Roberto Medeiros de Souza, Rafael de Oliveira Werneck, Bernardo V. Stein, Daniel V. Pazinato, Waldir R. de Almeida, Otávio A. B. Penatti, Ricardo da Silva Torres, Anderson Rocha:
Nearest neighbors distance ratio open-set classifier. 359-386 - Dongwoo Kim, Alice Oh:
Hierarchical Dirichlet scaling process. 387-418 - Jie Shen, Huan Xu, Ping Li:
Online optimization for max-norm regularization. 419-457
Volume 106, Number 4, April 2017
- Geoffrey Holmes, Tie-Yan Liu, Hang Li, Irwin King, Masashi Sugiyama, Zhi-Hua Zhou:
Introduction: special issue of selected papers from ACML 2015. 459-461 - Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Class-prior estimation for learning from positive and unlabeled data. 463-492 - Inbal Horev, Florian Yger, Masashi Sugiyama:
Geometry-aware principal component analysis for symmetric positive definite matrices. 493-522 - Shaowu Liu, Gang Li, Truyen Tran, Yuan Jiang:
Preference Relation-based Markov Random Fields for Recommender Systems. 523-546 - Shaowu Liu, Gang Li, Truyen Tran, Yuan Jiang:
Erratum to: Preference Relation-based Markov Random Fields for Recommender Systems. 547 - Wojciech Kotlowski, Krzysztof Dembczynski:
Surrogate regret bounds for generalized classification performance metrics. 549-572 - Fei Yu, Min-Ling Zhang:
Maximum margin partial label learning. 573-593 - Yiu-ming Cheung, Jian Lou:
Proximal average approximated incremental gradient descent for composite penalty regularized empirical risk minimization. 595-622
Volume 106, Number 5, May 2017
- Robert J. Durrant, Kee-Eung Kim, Geoffrey Holmes, Stephen Marsland, Masashi Sugiyama, Zhi-Hua Zhou:
Foreword: special issue for the journal track of the 8th Asian conference on machine learning (ACML 2016). 623-625 - Qi Mao, Li Wang, Ivor W. Tsang:
A unified probabilistic framework for robust manifold learning and embedding. 627-650 - Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun:
Collaborative topic regression for online recommender systems: an online and Bayesian approach. 651-670 - Yuping Wu, Hsuan-Tien Lin:
Progressive random k-labelsets for cost-sensitive multi-label classification. 671-694 - Sen Yang, Lijun Zhang:
Non-redundant multiple clustering by nonnegative matrix factorization. 695-712 - Sahely Bhadra, Samuel Kaski, Juho Rousu:
Multi-view kernel completion. 713-739
Volume 106, Number 6, June 2017
- Nathalie Japkowicz, Stan Matwin:
Special issue on discovery science. 741-743 - Aljaz Osojnik, Pance Panov, Saso Dzeroski:
Multi-label classification via multi-target regression on data streams. 745-770 - Pawel Matuszyk, Myra Spiliopoulou:
Stream-based semi-supervised learning for recommender systems. 771-798 - Morteza Zihayat, Yan Chen, Aijun An:
Memory-adaptive high utility sequential pattern mining over data streams. 799-836 - Mohammed Ghesmoune, Hanene Azzag, Salima Benbernou, Mustapha Lebbah, Tarn Duong, Mourad Ouziri:
Big Data: from collection to visualization. 837-862 - Simon Cousins, John Shawe-Taylor:
High-probability minimax probability machines. 863-886 - Carlos Eduardo Cancino Chacón, Thassilo Gadermaier, Gerhard Widmer, Maarten Grachten:
An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music. 887-909 - Daniel Berrar:
Confidence curves: an alternative to null hypothesis significance testing for the comparison of classifiers. 911-949
Volume 106, Number 7, July 2017
- Arkajyoti Saha, Swagatam Das:
Feature-weighted clustering with inner product induced norm based dissimilarity measures: an optimization perspective. 951-992 - Jesse H. Krijthe, Marco Loog:
Projected estimators for robust semi-supervised classification. 993-1008 - Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Masayuki Karasuyama, Ichiro Takeuchi:
Homotopy continuation approaches for robust SV classification and regression. 1009-1038 - Dimitris Bertsimas, Jack Dunn:
Optimal classification trees. 1039-1082 - Ran Tian, Naoaki Okazaki, Kentaro Inui:
The mechanism of additive composition. 1083-1130
Volume 106, Number 8, August 2017
- Céline Rouveirol, Ruggero G. Pensa, Rushed Kanawati:
Introduction to the special issue on dynamic networks and knowledge discovery. 1131-1132 - Venkata M. V. Gunturi, Shashi Shekhar, Kenneth Joseph, Kathleen M. Carley:
Scalable computational techniques for centrality metrics on temporally detailed social network. 1133-1169 - Mehdi Kaytoue, Marc Plantevit, Albrecht Zimmermann, Ahmed Anes Bendimerad, Céline Robardet:
Exceptional contextual subgraph mining. 1171-1211 - Giulio Rossetti, Luca Pappalardo, Dino Pedreschi, Fosca Giannotti:
Tiles: an online algorithm for community discovery in dynamic social networks. 1213-1241
Volume 106, Numbers 9-10, October 2017
- Kurt Driessens, Dragi Kocev, Marko Robnik-Sikonja, Myra Spiliopoulou:
Introduction to the special issue dedicated to the Journal Track of ECML PKDD 2017. 1243-1244 - Michele Donini, Fabio Aiolli:
Learning deep kernels in the space of dot product polynomials. 1245-1269 - Tijana Vujicic, Jesse Glass, Fang Zhou, Zoran Obradovic:
Gaussian conditional random fields extended for directed graphs. 1271-1288 - Nayyar Abbas Zaidi, Geoffrey I. Webb, Mark James Carman, François Petitjean, Wray L. Buntine, Mike Hynes, Hans De Sterck:
Efficient parameter learning of Bayesian network classifiers. 1289-1329 - Lavanya Sita Tekumalla, Vaibhav Rajan, Chiranjib Bhattacharyya:
Vine copulas for mixed data : multi-view clustering for mixed data beyond meta-Gaussian dependencies. 1331-1357 - Björn Weghenkel, Asja Fischer, Laurenz Wiskott:
Graph-based predictable feature analysis. 1359-1380 - Beilun Wang, Ritambhara Singh, Yanjun Qi:
A constrained $$\ell $$ ℓ 1 minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models. 1381-1417 - Matthias Bussas, Christoph Sawade, Nicolas Kühn, Tobias Scheffer, Niels Landwehr:
Varying-coefficient models for geospatial transfer learning. 1419-1440 - Samuel Kolb, Sergey Paramonov, Tias Guns, Luc De Raedt:
Learning constraints in spreadsheets and tabular data. 1441-1468 - Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfahringer, Geoff Holmes, Talel Abdessalem:
Adaptive random forests for evolving data stream classification. 1469-1495 - Toon van Craenendonck, Hendrik Blockeel:
Constraint-based clustering selection. 1497-1521 - Sebastijan Dumancic, Hendrik Blockeel:
An expressive dissimilarity measure for relational clustering using neighbourhood trees. 1523-1545 - Douglas de O. Cardoso, João Gama, Felipe M. G. França:
Weightless neural networks for open set recognition. 1547-1567 - Devin Schwab, Soumya Ray:
Offline reinforcement learning with task hierarchies. 1569-1598 - Pedram Daee, Tomi Peltola, Marta Soare, Samuel Kaski:
Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction. 1599-1620 - Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft:
Sparse probit linear mixed model. 1621-1642 - Matthew J. Holland, Kazushi Ikeda:
Robust regression using biased objectives. 1643-1679 - NhatHai Phan, Xintao Wu, Dejing Dou:
Preserving differential privacy in convolutional deep belief networks. 1681-1704 - Herke van Hoof, Daniel Tanneberg, Jan Peters:
Generalized exploration in policy search. 1705-1724 - Kuan-Hao Huang, Hsuan-Tien Lin:
Cost-sensitive label embedding for multi-label classification. 1725-1746 - Alon Zweig, Gal Chechik:
Group online adaptive learning. 1747-1770
Volume 106, Number 11, November 2017
- Michael Grabchak, Zhiyi Zhang:
Asymptotic properties of Turing's formula in relative error. 1771-1785 - Junyu Xuan, Jie Lu, Guangquan Zhang, Richard Yi Da Xu, Xiangfeng Luo:
A Bayesian nonparametric model for multi-label learning. 1787-1815 - Giorgio Corani, Alessio Benavoli, Janez Demsar, Francesca Mangili, Marco Zaffalon:
Statistical comparison of classifiers through Bayesian hierarchical modelling. 1817-1837 - Masanori Kawakita, Jun'ichi Takeuchi:
A note on model selection for small sample regression. 1839-1862
Volume 106, Number 12, December 2017
- Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto:
Special issue on inductive logic programming. 1863-1865 - Sergey Paramonov, Matthijs van Leeuwen, Luc De Raedt:
Relational data factorization. 1867-1904 - Davide Nitti, Vaishak Belle, Tinne De Laet, Luc De Raedt:
Planning in hybrid relational MDPs. 1905-1932 - Francesco Orsini, Paolo Frasconi, Luc De Raedt:
kProbLog: an algebraic Prolog for machine learning. 1933-1969 - Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock:
Soft quantification in statistical relational learning. 1971-1991
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