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» Distributed Regression for Heterogeneous Data Sets
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ML
2002
ACM
178views Machine Learning» more  ML 2002»
13 years 8 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
CCGRID
2006
IEEE
13 years 10 months ago
Biodiversity World: A Problem-Solving Environment for Analysing Biodiversity Patterns
In the Biodiversity World (BDW) project we have created a flexible and extensible Web Services-based Grid environment for biodiversity researchers to solve problems in biodiversity...
Jaspreet Singh Pahwa, Peter Brewer, Tim Sutton, Ch...
KDD
2008
ACM
159views Data Mining» more  KDD 2008»
14 years 8 months ago
Semi-supervised learning with data calibration for long-term time series forecasting
Many time series prediction methods have focused on single step or short term prediction problems due to the inherent difficulty in controlling the propagation of errors from one ...
Haibin Cheng, Pang-Ning Tan
UAI
2003
13 years 9 months ago
On Information Regularization
We formulate a principle for classification with the knowledge of the marginal distribution over the data points (unlabeled data). The principle is cast in terms of Tikhonov styl...
Adrian Corduneanu, Tommi Jaakkola
ICML
2009
IEEE
14 years 9 months ago
Detecting the direction of causal time series
We propose a method that detects the true direction of time series, by fitting an autoregressive moving average model to the data. Whenever the noise is independent of the previou...
Arthur Gretton, Bernhard Schölkopf, Dominik J...