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» A distributed machine learning framework
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CIKM
2008
Springer
14 years 19 days ago
Trada: tree based ranking function adaptation
Machine Learned Ranking approaches have shown successes in web search engines. With the increasing demands on developing effective ranking functions for different search domains, ...
Keke Chen, Rongqing Lu, C. K. Wong, Gordon Sun, La...
COLT
1993
Springer
14 years 2 months ago
Bounding the Vapnik-Chervonenkis Dimension of Concept Classes Parameterized by Real Numbers
The Vapnik-Chervonenkis (V-C) dimension is an important combinatorial tool in the analysis of learning problems in the PAC framework. For polynomial learnability, we seek upper bou...
Paul W. Goldberg, Mark Jerrum
SPAA
2006
ACM
14 years 4 months ago
On the price of heterogeneity in parallel systems
Suppose we have a parallel or distributed system whose nodes have limited capacities, such as processing speed, bandwidth, memory, or disk space. How does the performance of the s...
Brighten Godfrey, Richard M. Karp
ILP
1999
Springer
14 years 2 months ago
Probabilistic Relational Models
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning...
Daphne Koller
GECCO
2010
Springer
191views Optimization» more  GECCO 2010»
13 years 10 months ago
Fitness importance for online evolution
To complement standard fitness functions, we propose "Fitness Importance" (FI) as a novel meta-heuristic for online learning systems. We define FI and show how it can be...
Philip Valencia, Raja Jurdak, Peter Lindsay