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ML
2010
ACM
151views Machine Learning» more  ML 2010»
14 years 8 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
KDD
2009
ACM
230views Data Mining» more  KDD 2009»
15 years 10 months ago
Cross domain distribution adaptation via kernel mapping
When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the...
ErHeng Zhong, Wei Fan, Jing Peng, Kun Zhang, Jiang...
INFOCOM
2009
IEEE
15 years 4 months ago
Delay-Optimal Opportunistic Scheduling and Approximations: The Log Rule
—This paper considers the design of opportunistic packet schedulers for users sharing a time-varying wireless channel from the performance and the robustness points of view. Firs...
Bilal Sadiq, Seung Jun Baek, Gustavo de Veciana
JELIA
2004
Springer
15 years 3 months ago
Practical Reasoning for Uncertain Agents
Logical formalisation of agent behaviour is desirable, not only in order to provide a clear semantics of agent-based systems, but also to provide the foundation for sophisticated r...
Nivea de Carvalho Ferreira, Michael Fisher, Wiebe ...
PODC
1999
ACM
15 years 2 months ago
Optimal, Distributed Decision-Making: The Case of no Communication
We present a combinatorial framework for the study of a natural class of distributed optimization problems that involve decisionmaking by a collection of n distributed agents in th...
Marios Mavronicolas, Paul G. Spirakis