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ORCIDMachine Learning, Volume 105
Volume 105, Number 1, October 2016
- Saso Dzeroski
, Dragi Kocev, Pance Panov:
Special issue on discovery science. 1-2 - Prem Raj Adhikari, Anze Vavpetic, Jan Kralj, Nada Lavrac, Jaakko Hollmén:
Explaining mixture models through semantic pattern mining and banded matrix visualization. 3-39 - Matthijs van Leeuwen, Tijl De Bie, Eirini Spyropoulou, Cédric Mesnage:
Subjective interestingness of subgraph patterns. 41-75 - Eneldo Loza Mencía, Frederik Janssen:
Learning rules for multi-label classification: a stacking and a separate-and-conquer approach. 77-126 - Rita P. Ribeiro, Pedro Mota Pereira, João Gama:
Sequential anomalies: a study in the Railway Industry. 127-153
Volume 105, Number 2, November 2016
- Xiu-Shen Wei, Zhi-Hua Zhou:
An empirical study on image bag generators for multi-instance learning. 155-198 - Young Woong Park, Diego Klabjan:
An aggregate and iterative disaggregate algorithm with proven optimality in machine learning. 199-232 - Suleiman A. Khan, Eemeli Leppäaho, Samuel Kaski:
Bayesian multi-tensor factorization. 233-253 - Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen:
Learning deterministic probabilistic automata from a model checking perspective. 255-299 - Ye Zhu, Kai Ming Ting:
Commentary: a decomposition of the outlier detection problem into a set of supervised learning problems. 301-304
Volume 105, Number 3, December 2016
- Markus Schneider, Wolfgang Ertel, Fabio Ramos:
Expected similarity estimation for large-scale batch and streaming anomaly detection. 305-333 - Junichiro Hirayama, Aapo Hyvärinen, Shin Ishii:
Sparse and low-rank matrix regularization for learning time-varying Markov networks. 335-366 - Prashanth L. A., Mohammad Ghavamzadeh:
Variance-constrained actor-critic algorithms for discounted and average reward MDPs. 367-417 - Alex Foss, Marianthi Markatou, Bonnie Ray, Aliza Heching:
A semiparametric method for clustering mixed data. 419-458 - Erfan Soltanmohammadi, Mort Naraghi-Pour, Mihaela van der Schaar:
Context-based unsupervised ensemble learning and feature ranking. 459-485
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