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Publication search results
found 14 matches
- 2009
- Daniel Aloise, Amit Deshpande, Pierre Hansen, Preyas Popat:
NP-hardness of Euclidean sum-of-squares clustering. Mach. Learn. 75(2): 245-248 (2009) - Hal Daumé III, John Langford, Daniel Marcu:
Search-based structured prediction. Mach. Learn. 75(3): 297-325 (2009) - Thomas Gärtner, Gemma C. Garriga:
Guest editors' introduction: special issue on mining and learning with graphs. Mach. Learn. 75(1): 1-2 (2009) - Stéphanie Jacquemont, François Jacquenet, Marc Sebban:
Mining probabilistic automata: a statistical view of sequential pattern mining. Mach. Learn. 75(1): 91-127 (2009) - Pierre Mahé, Jean-Philippe Vert:
Graph kernels based on tree patterns for molecules. Mach. Learn. 75(1): 3-35 (2009) - Pekka Marttinen, Jukka Corander:
Bayesian learning of graphical vector autoregressions with unequal lag-lengths. Mach. Learn. 75(2): 217-243 (2009) - Andreas Maurer:
Transfer bounds for linear feature learning. Mach. Learn. 75(3): 327-350 (2009) - Tapio Pahikkala, Evgeni Tsivtsivadze, Antti Airola, Jouni Järvinen, Jorma Boberg:
An efficient algorithm for learning to rank from preference graphs. Mach. Learn. 75(1): 129-165 (2009) - Hiroto Saigo, Sebastian Nowozin, Tadashi Kadowaki, Taku Kudo, Koji Tsuda:
gBoost: a mathematical programming approach to graph classification and regression. Mach. Learn. 75(1): 69-89 (2009) - Masashi Shimbo, Takahiko Ito, Daichi Mochihashi, Yuji Matsumoto:
On the properties of von Neumann kernels for link analysis. Mach. Learn. 75(1): 37-67 (2009) - Masashi Sugiyama, Shinichi Nakajima:
Pool-based active learning in approximate linear regression. Mach. Learn. 75(3): 249-274 (2009) - Thorsten Suttorp, Nikolaus Hansen, Christian Igel:
Efficient covariance matrix update for variable metric evolution strategies. Mach. Learn. 75(2): 167-197 (2009) - Kazuho Watanabe, Motoki Shiga, Sumio Watanabe:
Upper bound for variational free energy of Bayesian networks. Mach. Learn. 75(2): 199-215 (2009) - Bin Zou, Luoqing Li, Zongben Xu:
The generalization performance of ERM algorithm with strongly mixing observations. Mach. Learn. 75(3): 275-295 (2009)
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