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ORCIDMachine Learning, Volume 93
Volume 93, Number 1, October 2013
- Hendrik Blockeel
, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Guest editor's introduction: special issue of the ECML PKDD 2013 journal track. 1-3 - Nicola Barbieri, Giuseppe Manco, Ettore Ritacco, Marco Carnuccio, Antonio Bevacqua:
Probabilistic topic models for sequence data. 5-29 - Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara:
Block coordinate descent algorithms for large-scale sparse multiclass classification. 31-52 - Kai Brügge, Asja Fischer, Christian Igel:
The flip-the-state transition operator for restricted Boltzmann machines. 53-69 - José Hernández-Orallo, Peter A. Flach, César Ferri:
ROC curves in cost space. 71-91 - Maurizio Filippone, Mingjun Zhong, Mark A. Girolami:
A comparative evaluation of stochastic-based inference methods for Gaussian process models. 93-114 - Nico Piatkowski, Sangkyun Lee, Katharina Morik:
Spatio-temporal random fields: compressible representation and distributed estimation. 115-139 - Quan Sun, Bernhard Pfahringer:
Pairwise meta-rules for better meta-learning-based algorithm ranking. 141-161 - Zhanglong Ji, Charles Elkan:
Differential privacy based on importance weighting. 163-183
Volume 93, Numbers 2-3, November 2013
- Eyke Hüllermeier, Johannes Fürnkranz:
Editorial: Preference learning and ranking. 185-189 - Mihajlo Grbovic, Nemanja Djuric, Shengbo Guo, Slobodan Vucetic:
Supervised clustering of label ranking data using label preference information. 191-225 - Clément Calauzènes, Nicolas Usunier, Patrick Gallinari:
Calibration and regret bounds for order-preserving surrogate losses in learning to rank. 227-260 - Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas:
Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers. 261-292 - Levente Kocsis, András György, Andrea N. Bán:
BoostingTree: parallel selection of weak learners in boosting, with application to ranking. 293-320 - Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman:
Efficient regularized least-squares algorithms for conditional ranking on relational data. 321-356 - Benjamin Letham, Cynthia Rudin, David Madigan:
Sequential event prediction. 357-380 - Salvatore Corrente, Salvatore Greco, Milosz Kadzinski, Roman Slowinski:
Robust ordinal regression in preference learning and ranking. 381-422
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