dblp: Machine Learning, Volume 88 (original) (raw)

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Volume 88, Numbers 1-2, July 2012

Special Issue on Learning from Multi-Label Data; Guest Editors: Grigorios Tsoumakas, Min-Ling Zhang, and Zhi-Hua Zhou

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    Grigorios Tsoumakas, Min-Ling Zhang, Zhi-Hua Zhou:
    Introduction to the special issue on learning from multi-label data. 1-4
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    Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier:
    On label dependence and loss minimization in multi-label classification. 5-45
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    Yiming Yang, Siddharth Gopal:
    Multilabel classification with meta-level features in a learning-to-rank framework. 47-68
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    Tianyi Zhou, Dacheng Tao, Xindong Wu:
    Compressed labeling on distilled labelsets for multi-label learning. 69-126
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    Bharath Hariharan, S. V. N. Vishwanathan, Manik Varma:
    Efficient max-margin multi-label classification with applications to zero-shot learning. 127-155
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    Timothy N. Rubin, America Chambers, Padhraic Smyth, Mark Steyvers:
    Statistical topic models for multi-label document classification. 157-208
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    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
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    Jesse Read, Albert Bifet, Geoff Holmes, Bernhard Pfahringer:
    Scalable and efficient multi-label classification for evolving data streams. 243-272
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    Jianjun He, Hong Gu, Zhelong Wang:
    Bayesian multi-instance multi-label learning using Gaussian process prior. 273-295

Volume 88, Number 3, September 2012

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    Masayuki Karasuyama, Naoyuki Harada, Masashi Sugiyama, Ichiro Takeuchi:
    Multi-parametric solution-path algorithm for instance-weighted support vector machines. 297-330
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    Oliver Schulte, Hassan Khosravi:
    Learning graphical models for relational data via lattice search. 331-368
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    Allison Chang, Cynthia Rudin, Michael Cavaretta, Robert Thomas, Gloria Chou:
    How to reverse-engineer quality rankings. 369-398
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    Ming-Wei Chang, Lev-Arie Ratinov, Dan Roth:
    Structured learning with constrained conditional models. 399-431
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    Johan Lim, Joong-Ho Won:
    ROC convex hull and nonparametric maximum likelihood estimation. 433-444

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