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ORCIDMachine Learning, Volume 79, 2010
Volume 79, Numbers 1-2, May 2010
- Nicolò Cesa-Bianchi, David R. Hardoon, Gayle Leen:
Guest Editorial: Learning from multiple sources. 1-3 - Felix Bießmann, Frank C. Meinecke, Arthur Gretton
, Alexander Rauch, Gregor Rainer, Nikos K. Logothetis, Klaus-Robert Müller:
Temporal kernel CCA and its application in multimodal neuronal data analysis. 5-27 - David R. Hardoon, John Shawe-Taylor:
Decomposing the tensor kernel support vector machine for neuroscience data with structured labels. 29-46 - Virginia R. de Sa, Patrick W. Gallagher, Joshua M. Lewis, Vicente L. Malave:
Multi-view kernel construction. 47-71 - Marie Szafranski, Yves Grandvalet, Alain Rakotomamonjy:
Composite kernel learning. 73-103 - Massih-Reza Amini, Cyril Goutte:
A co-classification approach to learning from multilingual corpora. 105-121 - Mark Dredze, Alex Kulesza, Koby Crammer:
Multi-domain learning by confidence-weighted parameter combination. 123-149 - Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan:
A theory of learning from different domains. 151-175 - Vikas Singh, Lopamudra Mukherjee, Jiming Peng, Jinhui Xu:
Ensemble clustering using semidefinite programming with applications. 177-200 - Simon Rogers, Arto Klami, Janne Sinkkonen, Mark A. Girolami, Samuel Kaski:
Infinite factorization of multiple non-parametric views. 201-226 - Roger Luis, Luis Enrique Sucar, Eduardo F. Morales:
Inductive transfer for learning Bayesian networks. 227-255
Volume 79, Number 3, June 2010
- Dragos D. Margineantu, Weng-Keen Wong, Denver Dash:
Machine learning algorithms for event detection. 257-259 - Daniel B. Neill, Gregory F. Cooper:
A multivariate Bayesian scan statistic for early event detection and characterization. 261-282 - Daniel Nikovski, Ankur Jain:
Fast adaptive algorithms for abrupt change detection. 283-306 - Gaurav Tandon, Philip K. Chan:
Increasing coverage to improve detection of network and host anomalies. 307-334 - Tomás Singliar, Milos Hauskrecht:
Learning to detect incidents from noisily labeled data. 335-354
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