dblp: Machine Learning, Volume 93 (original) (raw)

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Volume 93, Number 1, October 2013

Special Issue of the ECML PKDD 2013 Journal Track

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    Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
    Guest editor's introduction: special issue of the ECML PKDD 2013 journal track. 1-3
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    Nicola Barbieri, Giuseppe Manco, Ettore Ritacco, Marco Carnuccio, Antonio Bevacqua:
    Probabilistic topic models for sequence data. 5-29
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    Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara:
    Block coordinate descent algorithms for large-scale sparse multiclass classification. 31-52
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    Kai Brügge, Asja Fischer, Christian Igel:
    The flip-the-state transition operator for restricted Boltzmann machines. 53-69
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    José Hernández-Orallo, Peter A. Flach, César Ferri:
    ROC curves in cost space. 71-91
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    Maurizio Filippone, Mingjun Zhong, Mark A. Girolami:
    A comparative evaluation of stochastic-based inference methods for Gaussian process models. 93-114
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    Nico Piatkowski, Sangkyun Lee, Katharina Morik:
    Spatio-temporal random fields: compressible representation and distributed estimation. 115-139
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    Quan Sun, Bernhard Pfahringer:
    Pairwise meta-rules for better meta-learning-based algorithm ranking. 141-161
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    Zhanglong Ji, Charles Elkan:
    Differential privacy based on importance weighting. 163-183

Volume 93, Numbers 2-3, November 2013

Preference Learning and Ranking

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    Eyke Hüllermeier, Johannes Fürnkranz:
    Editorial: Preference learning and ranking. 185-189
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    Mihajlo Grbovic, Nemanja Djuric, Shengbo Guo, Slobodan Vucetic:
    Supervised clustering of label ranking data using label preference information. 191-225
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    Clément Calauzènes, Nicolas Usunier, Patrick Gallinari:
    Calibration and regret bounds for order-preserving surrogate losses in learning to rank. 227-260
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    Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas:
    Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers. 261-292
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    Levente Kocsis, András György, Andrea N. Bán:
    BoostingTree: parallel selection of weak learners in boosting, with application to ranking. 293-320
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    Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman:
    Efficient regularized least-squares algorithms for conditional ranking on relational data. 321-356
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    Benjamin Letham, Cynthia Rudin, David Madigan:
    Sequential event prediction. 357-380
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    Salvatore Corrente, Salvatore Greco, Milosz Kadzinski, Roman Slowinski:
    Robust ordinal regression in preference learning and ranking. 381-422

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