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» Learning from Highly Structured Data by Decomposition
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2002
IEEE
14 years 9 months ago
Open Metadata Formats: Efficient XML-Based Communication for High Performance Computing
High-performance computing faces considerable change as the Internet and the Grid mature. Applications that once were tightly-coupled and monolithic are now decentralized, with co...
Patrick Widener, Greg Eisenhauer, Karsten Schwan, ...
103
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IJCAI
2003
14 years 11 months ago
Statistics Gathering for Learning from Distributed, Heterogeneous and Autonomous Data Sources
With the growing use of distributed information networks, there is an increasing need for algorithmic and system solutions for data-driven knowledge acquisition using distributed,...
Doina Caragea, Jaime Reinoso, Adrian Silvescu, Vas...
BMCBI
2008
166views more  BMCBI 2008»
14 years 9 months ago
Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual informa
Background: Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mecha...
Weijun Luo, Kurt D. Hankenson, Peter J. Woolf
ICGI
1998
Springer
15 years 1 months ago
Learning k-Variable Pattern Languages Efficiently Stochastically Finite on Average from Positive Data
Abstract. The present paper presents a new approach of how to convert Gold-style [4] learning in the limit into stochastically finite learning with high confidence. We illustrate t...
Peter Rossmanith, Thomas Zeugmann
ECML
2007
Springer
15 years 3 months ago
Structure Learning of Probabilistic Relational Models from Incomplete Relational Data
Abstract. Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn...
Xiao-Lin Li, Zhi-Hua Zhou