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ORCIDMachine Learning, Volume 88
Volume 88, Numbers 1-2, July 2012
- Grigorios Tsoumakas
, Min-Ling Zhang, Zhi-Hua Zhou:
Introduction to the special issue on learning from multi-label data. 1-4 - Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier:
On label dependence and loss minimization in multi-label classification. 5-45 - Yiming Yang, Siddharth Gopal:
Multilabel classification with meta-level features in a learning-to-rank framework. 47-68 - Tianyi Zhou, Dacheng Tao, Xindong Wu:
Compressed labeling on distilled labelsets for multi-label learning. 69-126 - Bharath Hariharan, S. V. N. Vishwanathan, Manik Varma:
Efficient max-margin multi-label classification with applications to zero-shot learning. 127-155 - Timothy N. Rubin, America Chambers, Padhraic Smyth, Mark Steyvers:
Statistical topic models for multi-label document classification. 157-208 - Nicolò Cesa-Bianchi, Matteo Re, Giorgio Valentini:
Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference. 209-241 - Jesse Read, Albert Bifet, Geoff Holmes, Bernhard Pfahringer:
Scalable and efficient multi-label classification for evolving data streams. 243-272 - Jianjun He, Hong Gu, Zhelong Wang:
Bayesian multi-instance multi-label learning using Gaussian process prior. 273-295
Volume 88, Number 3, September 2012
- Masayuki Karasuyama, Naoyuki Harada, Masashi Sugiyama, Ichiro Takeuchi:
Multi-parametric solution-path algorithm for instance-weighted support vector machines. 297-330 - Oliver Schulte, Hassan Khosravi:
Learning graphical models for relational data via lattice search. 331-368 - Allison Chang, Cynthia Rudin, Michael Cavaretta, Robert Thomas, Gloria Chou:
How to reverse-engineer quality rankings. 369-398 - Ming-Wei Chang, Lev-Arie Ratinov, Dan Roth:
Structured learning with constrained conditional models. 399-431 - Johan Lim, Joong-Ho Won:
ROC convex hull and nonparametric maximum likelihood estimation. 433-444
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