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» Evaluating algorithms that learn from data streams
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KDD
2009
ACM
230views Data Mining» more  KDD 2009»
15 years 11 months ago
Grouped graphical Granger modeling methods for temporal causal modeling
We develop and evaluate an approach to causal modeling based on time series data, collectively referred to as“grouped graphical Granger modeling methods.” Graphical Granger mo...
Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Ros...
KDD
2009
ACM
229views Data Mining» more  KDD 2009»
16 years 7 months ago
Relational learning via latent social dimensions
Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather than classical IID distribution. To address the interdependency among data instances, ...
Lei Tang, Huan Liu
ENVSOFT
2007
54views more  ENVSOFT 2007»
15 years 6 months ago
Evaluation of landscape and instream modeling to predict watershed nutrient yields
The project goal was to loosely couple the SWAT model and the QUAL2E model and compare their combined ability to predict total phosphorus (TP) and NO3-N plus NO2-N yields to the a...
K. W. Migliaccio, I. Chaubey, B. E. Haggard
FLAIRS
1998
15 years 8 months ago
Lazy Transformation-Based Learning
Weintroduce a significant improvementfor a relatively newmachine learning methodcalled Transformation-Based Learning. By applying a MonteCarlo strategy to randomly sample from the...
Ken Samuel
SIGMOD
2004
ACM
262views Database» more  SIGMOD 2004»
16 years 7 months ago
The Next Database Revolution
Database system architectures are undergoing revolutionary changes. Most importantly, algorithms and data are being unified by integrating programming languages with the database ...
Jim Gray