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