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EMNLP
2011
13 years 9 months ago
Structured Sparsity in Structured Prediction
Linear models have enjoyed great success in structured prediction in NLP. While a lot of progress has been made on efficient training with several loss functions, the problem of ...
André F. T. Martins, Noah A. Smith, M&aacut...
85
Voted
SAC
2008
ACM
14 years 9 months ago
Local linear regression with adaptive orthogonal fitting for the wind power application
For short-term forecasting of wind generation, a necessary step is to model the function for the conversion of meteorological variables (mainly wind speed) to power production. Su...
Pierre Pinson, Henrik Aalborg Nielsen, Henrik Mads...
PRIMA
2009
Springer
15 years 4 months ago
Adaptation and Validation of an Agent Model of Functional State and Performance for Individuals
Human performance can seriously degrade under demanding tasks. To improve performance, agents can reason about the current state of the human, and give the most appropriate and eff...
Fiemke Both, Mark Hoogendoorn, S. Waqar Jaffry, Ri...
85
Voted
KDD
2004
ACM
179views Data Mining» more  KDD 2004»
15 years 10 months ago
1-dimensional splines as building blocks for improving accuracy of risk outcomes models
Transformation of both the response variable and the predictors is commonly used in fitting regression models. However, these transformation methods do not always provide the maxi...
David S. Vogel, Morgan C. Wang
PAKDD
2000
ACM
161views Data Mining» more  PAKDD 2000»
15 years 1 months ago
Adaptive Boosting for Spatial Functions with Unstable Driving Attributes
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipu...
Aleksandar Lazarevic, Tim Fiez, Zoran Obradovic