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» Evaluating algorithms that learn from data streams
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KDD
2009
ACM
191views Data Mining» more  KDD 2009»
16 years 5 months ago
Efficient methods for topic model inference on streaming document collections
Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of...
Limin Yao, David M. Mimno, Andrew McCallum
IJCAI
2007
15 years 6 months ago
Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions
We describe an approach to extract attribute-value pairs from product descriptions. This allows us to represent products as sets of such attribute-value pairs to augment product d...
Katharina Probst, Rayid Ghani, Marko Krema, Andrew...
EUROMICRO
1998
IEEE
15 years 8 months ago
Improved Multimedia Server I/O Subsystems
The main function of a continuous media server is to concurrently stream data from storage to multiple clients over a network. The resulting streams will congest the host CPU bus,...
Michael Weeks, Hadj Batatia, Reza Sotudeh
COMPGEOM
2011
ACM
14 years 8 months ago
Metric graph reconstruction from noisy data
Many real-world data sets can be viewed of as noisy samples of special types of metric spaces called metric graphs [16]. Building on the notions of correspondence and GromovHausdo...
Mridul Aanjaneya, Frédéric Chazal, D...
CIKM
2011
Springer
14 years 4 months ago
Toward interactive training and evaluation
Machine learning often relies on costly labeled data, and this impedes its application to new classification and information extraction problems. This has motivated the developme...
Gregory Druck, Andrew McCallum