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» Learning from Highly Structured Data by Decomposition
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UAI
1996
14 years 11 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
ACL
2006
14 years 11 months ago
Weakly Supervised Named Entity Transliteration and Discovery from Multilingual Comparable Corpora
Named Entity recognition (NER) is an important part of many natural language processing tasks. Current approaches often employ machine learning techniques and require supervised d...
Alexandre Klementiev, Dan Roth
103
Voted
SOCIALCOM
2010
14 years 8 months ago
Using Text Analysis to Understand the Structure and Dynamics of the World Wide Web as a Multi-Relational Graph
A representation of the World Wide Web as a directed graph, with vertices representing web pages and edges representing hypertext links, underpins the algorithms used by web search...
Harish Sethu, Alexander Yates
INFOCOM
2006
IEEE
15 years 4 months ago
Reverse Hashing for High-Speed Network Monitoring: Algorithms, Evaluation, and Applications
— A key function for network traffic monitoring and analysis is the ability to perform aggregate queries over multiple data streams. Change detection is an important primitive w...
Robert T. Schweller, Zhichun Li, Yan Chen, Yan Gao...
SIBGRAPI
2006
IEEE
15 years 4 months ago
Extracting Discriminative Information from Medical Images: A Multivariate Linear Approach
Statistical discrimination methods are suitable not only for classification but also for characterisation of differences between a reference group of patterns and the population u...
Carlos E. Thomaz, Nelson A. O. Aguiar, Sergio H. A...